JP2021519422A - Network architecture and methods for location services - Google Patents

Abstract

Disclose a split architecture for locating wireless devices in a heterogeneous wireless communication environment. A detector in the device or another component of the environment receives a signal that includes the parameters of the device's location signal. The parameters describe a previously known signal within the signal. Additional metadata is also received, including each frame start of the signal and support data and auxiliary information. The pre-known signal is detected based on the parameters of the position confirmation signal. Samples extracted from pre-known signals are then processed, compressed, and sent from the detector to a remote location server along with other collected data. The location server uses that information as well as similar information about the environment to calculate the location of the device, perform device tracking and navigation, and report the results to the environment.

Description

(Cross-reference of related applications)
This application is filed in U.S. Patent Provisional Application No. 62 / 653,450 filed April 5, 2018, file No. 62 / 648,883 filed March 27, 2018, and filed October 18, 2018. Claims priority over US Patent Application No. 16 / 164,724.

(Field of invention)
In this embodiment, wireless communication and wireless network systems, as well as radio frequency (RF) -based object identification, tracking, and locating, such as real-time locating services (RTLS), LTE-based locating services, and Location- It relates to a system for as-a-Service (Laas) and the like. The present embodiment also relates to a unified framework / platform (architecture, functional entities, and behavior). These will support positioning technologies, methods / techniques such as comprehensive wireless device radio signals, hybrid / fusion positioning.

RF-based identification and position detection systems for determining the relative or geographical location of an object are generally used to track a single object or group of objects, as well as to track an individual. Traditional position-fixing systems have been used for position-fixing in open outdoor environments. Typically, RF-based Global Positioning System (GPS) / Global Navigation Satellite System (GNSS), and Assisted GPS / GNSS are used. However, conventional position detection systems suffer from certain inaccuracies when locating objects in closed (ie, indoor) and outdoor environments.

Cellular radio communication systems provide various methods for locating user equipment (UEs) indoors and in less suitable environments for GPS. The most accurate method is a positioning technique based on multilateration / three-range multilateration. For example, Long Term Evolution (LTE) Standard Release 9 defines DL-OTDOA (Downlink Observation Arrival Time Difference), and Release 11 is U-TDOA (Uplink Arrival Time Difference) derived from the multilateration / three-range multilateration method. ) Define the technique.

Since time synchronization errors affect positioning accuracy, the basic requirement for a multilateration / ternary multilateration-based system is complete and accurate time synchronization of the system for a single common reference time. In cellular networks, the DL-OTDOA and U-TDOA localization methods also allow transmissions from multiple antennas to be time-synchronized in the case of DL-OTDOA, or multiple receivers in the case of U-TDOA. Requires time synchronization.

LTE standard releases 9 and 11 do not specify the accuracy of time synchronization for location identification, leaving this to the wireless / cellular service provider. On the other hand, these standards limit the distance measurement accuracy. For example, when using a ranging signal bandwidth of 10 MHz, DL-OTDOA is required to have 67% reliability for 50 meters, and U-TDOA is required to have 67% reliability for 100 meters.

The above limits are the result of a combination of ranging errors and errors caused by lack of precision synchronization, such as time synchronization errors. From the relevant LTE test specifications (3GPP TS 36.133 Version 10.1.0 Release 10) and other documents, it is possible to estimate the time synchronization error, assuming that the synchronization error is evenly distributed. be. One such estimate reaches 200 ns (100 ns peak-to-peak). Note that the Voice over LTE (VoLTE) function also requires cellular network synchronization up to 150 nanoseconds (75 ns peak-to-peak), assuming that the synchronization errors are evenly distributed. Therefore, in the future, the time synchronization accuracy of LTE networks will be assumed to be within 150 ns.

Regarding distance position accuracy, FCC Directive NG911 specifies position accuracy requirements for 50 meters and 100 meters. However, in the location-based services (LBS) market, indoor location requirements are much more stringent with 67% reliability for 3 meters. Therefore, the distance measurement error and position error caused by the time synchronization error of 150 ns (standard deviation of 43 ns) are much larger than the distance measurement error of 3 meters (standard deviation of 10 ns).

Time synchronization of cellular networks may be sufficient to meet the mandatory FCC NG E911 emergency location requirements, but this synchronization accuracy is not suitable for the needs of LBS or RTLS system users who require much more accurate location. It is enough. Therefore, there is a need in the art to mitigate locating errors caused by the lack of accurate time synchronization of cellular / wireless networks to support LBS and RTLS.

The LTE 4G deployment supports location-based services (LBS) that use the available location information of the terminal, while the proposed 5G deployment provides Location-as-a-Service (Laas) data delivery instead of LBS. to support. According to this model, location data is collected by the communication service carrier and made available to clients via the API. While this approach is intended to better protect the privacy of physical location data for enterprise customers and consumers, it also presents many problems.

Positioning in a 5G network can be performed either within the UE device (device-centric), network-centric, or both (according to the LTE standard). However, network-centric options have some advantages over other approaches. First, network-centric positioning allows for more advanced positioning techniques to be updated with computing power that far exceeds the computing power available in UE devices. Network-centric positioning also facilitates positioning algorithm upgrades and other changes. Positioning within the UE device tends to create a logistical load (on the carrier). This is because the carrier relies on the device manufacturer to make the necessary changes and the computational resources of the device are more limited. In addition, network-centric options allow the positioning engine to run continuously in the background, enabling ubiquitous high-precision positioning that provides the latest location information with low latency, while also indoors and Location information can be obtained anywhere in the network coverage area, including within the urban corridor environment. Also, since the network-centric option removes the heavy computational load required for positioning from the UE, network-centric positioning is more energy efficient from a device perspective and significantly improves the power consumption of the UE.

Specifically, improving the power consumption of the UE is very important for wireless modems targeting IoT (Internet of Things) applications. These modems may also use additional strategies (in addition to network-centric positioning) to save power.

In general, wireless networks use two positioning methods: uplink arrival time difference (TDOA) and downlink OTDOA. The uplink method uses the signal transmitted by the UE to the network element, and the downlink method uses the signal transmitted from the network element to the UE. Specifically, these network elements are various types of cells (macro, small, distributed, etc.) and the signal is a reference signal. Each method has its own trade-offs, and depending on the environment, one method may be more advantageous than the other. Both methods rely on the multilateration positioning method. However, in the case of downlink OTDOA, the cell tower transmission power is two orders of magnitude higher than the UE transmitter power. As a result, the downlink OTDOA method provides more ubiquitous coverage. At the same time, uplink positioning can also support the arrival angle (AoA) / arrival direction (DoA) method, where AoA / DoA is the uplink transmission from the UE and the eNodeB (also known as eNoB / eNB) antenna. Measured based on the known configuration of the array. This approach has the potential for higher positioning accuracy, which is highly dependent on the characteristics of the antenna in the particular cell. On the other hand, the uplink TDOA and AoA methods are essentially network-centric because the associated UE transmission can be received and collected by a network element such as eNoB.

Conventional embodiments of downlink OTDOA are either UE-assisted (eg, 3GPP 36.305 v14) or UE-based. The UE auxiliary scheme requires the UE to perform downlink timing measurements. These measurements include estimating the reference signal time difference (RSTD) between reference signals from multiple cells. These time differences are then reported to a network element called the Evolved Serving Mobile Center (E-SMLC) for further processing. As a result, a considerable amount of skill is required to reduce the downlink timing measurement error caused by the RF propagation phenomenon. Otherwise, the UE positioning accuracy will be greatly affected. However, in order to reduce such an error, it is necessary to use the state-of-the-art position-specific signal processing technology, which involves a high computational load on UE resources. Therefore, there is a need to improve downlink OTDOA performance without requiring the UE to have larger computational resources and without causing a logistical load on the carrier and UE manufacturer.

At the same time, a network-centric architecture that enables Location-as-a-Service (Laas) data distribution, as well as uplinks / downlinks or downlinks / uplinks that improve the reliability and location measurement accuracy of location systems. There is a need for an architecture that enables advanced features that were previously impossible, such as combined UE positioning. There is a further need for such an architecture that allows and enables multiple locating techniques to be performed simultaneously on the same data. Finally, such an architecture needs to be able to recognize more towers and infrastructure as the entire database of all network elements is available.

Wireless devices, also known as user equipment (UEs), utilize a wide range of technologies, including GPS, cellular signals, Wi-Fi (WLAN) signals, beacon signals, and various passive and active sensors such as gyro sensors. And acquire long-distance position information. Since a single technology cannot address all geographic environment and / or performance requirements, combine multiple technologies into hybrid positioning (also known as fusion positioning).

In addition, numerous positioning methods and / or techniques such as uplink, downlink, and extended cell ID positioning, auxiliary GNSS / GPS, triangulation, trilateration, multilation, RF fingerprinting, RSS (received signal strength), etc. Exists.

Not all existing wireless communication networks integrate multiple positioning technologies, but efforts towards integration are highest in cellular communication networks. However, the positioning architectures and capabilities of these networks are technology / method / technology specific and therefore capable of meeting changing industry / customer requirements and / or positioning technologies and methods / techniques such as hybrid / fusion positioning. Limit your ability to take advantage of progress.

This architectural fragmentation stems from current cellular network strategies that prefer low bandwidth when communicating with location servers. It collects a number of received signal strength indicators (RSSIs) for each network node visible at the location and sends this information (data) to a location server to determine target positioning, RF fingerprinting, etc. Explain the past popularity of stochastic positioning algorithms and methods. Over time, customers and applications have demanded higher levels of performance, including indoors (Radio E911 Position Accuracy Requirements from the Fourth Report and Order from the Federal Communications Commission (FCC)). This is the limit of RF fingerprinting, such as reduced accuracy in target movement scenarios, which requires persistently troublesome human involvement to build and update so-called fingerprint maps, and ambiguity in location confirmation. The existence of sex and error floor phenomena was revealed.

In response to the above demands for higher position accuracy and reliability, the radio industry has deployed methods and techniques with the potential for accurate positioning. The most widely used wireless communication networks are downlink and uplink positioning: arrival time (TOA), arrival time difference (TDOA), flight time (TOF), arrival angle (AOA), and received signal phase. Each technique of.

The possibility of higher accuracy also entails more sophisticated computational requirements. However, by acknowledging the low bandwidth approach, in existing wireless communication network architectures, wireless devices and / or specific (dedicated) network elements / components are TOA, TDOA (also known as RSTD / RTOA), TOF, AOA. Etc. and these metrics (ie, observation results) need to be used to determine the observation results that are sent to the network location confirmation server responsible for positioning calculations. This prevents the introduction of ever-evolving positioning algorithms / methods (due to computational resource constraints) and limits the further improvement of positioning performance to the computational resources of wireless devices and / or specific network components. Imposing a heavy load. Note that the computational constraints of wireless devices or specific (dedicated) network elements arise from limited size, cost, power consumption requirements, as well as logistical loads and HW / SW legacy constraints.

The wireless industry uses a combination of two or more technologies / methods, a hybrid approach, because a single location technology / method cannot be used to meet customer / application requirements. However, the gain of hybrid locating performance is incremental in nature and cannot meet all customer / application requirements. For example, combining GPS / GNSS and LTE DL OTDOA improves positioning in an outdoor environment, but does not bring improvement indoors because GPS / GNSS does not work in an indoor environment.

At the same time, hybrid location increases costs, power consumption and solution complexity. Newly emerging IoT (Internet of Things) applications require very low cost, small size, extremely low power consumption, low complexity, and ubiquitous coverage in a wide range of environments, as well as deep window wireless devices (sensors) in buildings. And. This is especially relevant for Cat-M and NBIOT IOT-based solutions where battery life is very important and the device is typically not robust on the board processor. Other issues include long downtime, short initial positioning time (TTFF), and narrow signal bandwidth that affect positioning accuracy. Subsequently, hybrid solutions cannot be used due to additional costs, power consumption, and increased size. In addition, mitigating the effects of narrow signal bandwidth combined with deep-window building environments requires state-of-the-art algorithms that are out of reach of IoT wireless devices and require extremely large amounts of computational power / resources. And. Therefore, there is no suitable location solution for low cost and long battery life.

Today's WLAN access points (APs) feature multiple antennas on transmitters and receivers, as well as signal structures that allow for state-of-the-art location. Therefore, it is conceivable to use hybrid positioning in an indoor environment using both Wi-Fi and LTE signals. However, state-of-the-art target locating is performed by the WLAN infrastructure, and LTE target locating is typically split between the wireless device and the LTE infrastructure positioning server. Also, the Wi-Fi and LTE hybrids will require additional processing to finalize the target position by combining the Wi-Fi intermediate results and the LTE intermediate results.

One option for wireless devices is to perform all Wi-Fi and LTE intermediate calculations and send the results to the LSU server to determine the target location. The other option for wireless devices is to perform all calculations to determine their unique location. Given the computational resource constraints of wireless devices, neither option is suitable for supporting advanced positioning techniques.

In addition, another option for the WLAN infrastructure is to provide the LSU with intermediate information and / or other location information to determine the location of the wireless device. However, this option imposes computational and logistical loads (algorithm / processing updates, WLAN and LTE tuning, etc.) on the WLAN infrastructure.

GPS / GNSS locating technology is an important part of wireless device locating solutions. GPS / GNSS does not work in indoor and dense urban environments, whereas its accuracy does not match in rural and suburban environments. In addition, the assisted GPS / GNSS (AGPS / AGNSS) method (mode) extends GPS / GNSS functionality to many high-density urban environments, albeit with reduced accuracy / reliability. The cellular network provides the necessary auxiliary data for wireless devices equipped with GPS / GNSS receivers so that they can operate in AGPS / AGNSS mode. These receivers are also responsible for determining observation results such as pseudo-range, pseudo-Doppler, etc. transmitted to the location server of the network where the location calculation is performed. Alternatively, the wireless device (GPS / GNSS) receiver can perform stand-alone operations and calculate its location with or without the assistance of a cellular network.

Therefore, network assisted GPS / GNSS operation depends on the signaling between the GPS / GNSS receiver of each wireless device and the network infrastructure, and at the same time requires a very large amount of computational resources to calculate the observation results and their metrics. Also required, and in stand-alone operating mode, even more computational resources are needed to calculate both the observations and the position of the device itself. Both options increase the cost, size, and power emissions of the receiver.

Other technologies such as Bluetooth and Terrestrial Beacon System are also used for location in wireless networks. Once integrated into a cellular network, due to the wireless device positioning low communication bandwidth strategy described above, these technology-specific features extend across wireless devices and network elements. This hinders future advances in wireless network positioning performance, fails to meet customer / application performance requirements, heavy computational load on wireless devices and / or network components, to support new advanced algorithms. It imposes a logistical load on network and HW / SW legacy constraints.

Advanced wireless network architectures, such as 5G (a unified, more powerful, new global 3GPP standard for wireless), are intended for Location-as-a-Service (LaaS) data distribution, thereby wireless devices. Acts as a gateway to the computing cloud and LaaS, especially for protected physical location data. However, the fragmentation of current positioning architectures is not suitable for supporting LaaS due to the above defects and security concerns of physical location data.

Therefore, the art is for a unified framework / platform (architecture, functional entities, and operations) that will support comprehensive wireless device radio signals, positioning technologies, including hybrid / fusion location, methods / techniques. There is a need. The main benefits are better location accuracy, reliability, energy efficient wireless device, Network-as-a-Service (LaaS) data distribution, enhanced security, promotion of new function development, logistic load and Expandable computing power that significantly exceeds the computing power available in the infrastructure elements of mobile devices and / or individual networks with reduced legacy constraints.

This disclosure substantially eliminates one or more of the disadvantages associated with existing systems for identifying, tracking, and locating radio frequency (RF) -based objects, such as real-time locating service (RTLS) systems. Regarding methods and systems for. The method and system can use partially synchronized (temporal) receivers and / or transmitters. According to one embodiment, RF-based tracking and relocation is performed within a cellular network, but can also be performed in any wireless system and RTLS environment. The proposed system can use software-defined digital signal processing and software defined radio (SDR) technology. Digital signal processing (DSP) can also be used.

One approach described herein uses clusters of receivers and / or transmitters that are precisely time synchronized within each cluster, but the precision of time synchronization between clusters is fairly low or required at all. It may not be. This embodiment can be used in all wireless systems / networks and includes simplex operating modes, half-duplex operating modes, and full-duplex operating modes. The embodiments described below operate in wireless networks using various modulation types such as OFDM modulation and / or derivatives thereof. Therefore, the embodiments described below operate in LTE networks and are also applicable to other wireless systems / networks.

As described in one embodiment, RF-based tracking and localization performed in a 3GPP LTE cellular network benefits significantly from precisely synchronized (temporal) receiver and / or transmitter clusters. The proposed system can use digital signal processing by software and / or hardware.

As described in some embodiments, the network-centric architecture supports LaaS data delivery, when all signal processing and location estimation is done in the cloud, i.e. outside the UE and / or eNodeB. Designed for 5G and other networks. There are several options as to how this can be done. For uplink location identification, the associated UE reference signal transmission is collected / preprocessed by eNodeB in the macro environment and LMU in other environments, transferred to the location server unit (LSU) for further processing, and the location of the UE. Can be determined. In the case of downlink (OTDOA), the task of collecting and preprocessing the downlink reference signal may be performed by the UE. The UE may then send the collected downlink data to the LSU. In the case of downlink (OTDOA), the UE may also use the control plane and / or the LTE user (data) plane to handle communication with the LSU. Therefore, signaling may be in accordance with the OMA Secure User Plane Localization (SUPL) protocol and / or 3GPP, such as the LTE Positioning Protocol (LPP). For uplink AoA / DoA, the eNodeB may use LPPa and SLmAP (SLm application protocol) protocols or MTTQ to process communication with the LSU.

As described in some embodiments, this network-centric architecture can enable advanced features previously not possible. For these functions, (a) downlink OTDOA is used to measure the distance between the serving cell / tower and the UE and the uplink AoA / DoA to determine the position of the UE, while the tracking algorithm / By using the technology, the influence of the OTDOA synchronization error on the uplink / downlink UE positioning can be reduced, and (b) RTT (round trip time) (also known as time advance (also known as time advance) ((TA)) in addition to the uplink AoA / DoA. Alternatively, when the UE position is determined using the TADV) method, the tracking algorithm / technique is used to improve the UE positioning, and (c) the tracking algorithm / technique is used in the downlink OTDOA positioning method. Estimating the synchronization error and correcting / reducing it.

As described in some embodiments, the downlink (OTDOA) UE positioning method includes a navigation processor that uses a multilateration technique / method (also known as hyperbolic navigation). This multilateration technique involves solving a large number of hyperbolic (RSTD / TDOA) equations. There are iterative and non-repetitive (closed forms) solutions. In one embodiment, a hybrid that divides the number of available (audible) reference points (base stations) into multiple sets of three RSTD / TDOA subsets and finds a closed form solution for each subset. The approach is described. The position determination can then be finalized using a position matching algorithm. In a second embodiment, positioning may be improved by utilizing a combination of iterative and non-repetitive solutions from the same set of RSTD / TDOA values. In a third embodiment, the uplink AoA / DoA estimates and RTT may be used to determine initial position estimates for UE positions based on iterative and non-repetitive algorithms.

As described in some embodiments, a wireless network environment requires at least three reference points to deliver a large amount of RF signals over a wide area using only two high power cells (2D localization). In many cases, position determination is obtained without following the multilateration method. Therefore, when RTT is available, the UE is located along the arc defined by the azimuth beamwidth of the serving sector and the RTT / 2 range. If AoA / DoA estimates are available, the UE position can be determined near the intersection of the hyperbola and the arc. Both of these methods may be used. If neither RTT nor AoA / DoA estimates are available, the UE position is determined by scoring the intersections corresponding to each cell / tower (sector) on the selected hyperbola and each tower (sector). ). Scoring is based on the angular difference between the direction the cell is facing and the direction to a point on the hyperbola, and the distance from each point to the corresponding cell / tower. Scores can be weighted according to the SNR of these corresponding cells / towers.

As described in some embodiments, the LSU may include a communication processor, eNodeB, and / or network elements that are configured for signaling and information exchange with the UE. Signaling conforms to the OMA SUPL protocol and / or 3GPP LPP / LPPa, or a combination of LPP, LPPa, and SUPL, and other protocols used or may be used to communicate with the network, such as the LCS-AP protocol. You can do it.

As described in some embodiments, the LSU components are stored in memory and configured to run on the processor of a 4.5G MEC (Mobile Edge Computing) server located at the edge of the communication network. It may be an instruction. LSU components may be integrated as hosted apps on 4.5G MEC. In 5G deployments, LSU components may be hosted within the core network computing cloud. LSUs hosted within the core network computing cloud may support LaaS data delivery, whereby the UE acts as a gateway to the core network computing cloud and LaaS dedicated to protected physical location data.

As described in some embodiments, the LSU may include a downlink signal processor as well as an uplink signal processor and a navigation processor.

As described in some embodiments, a unified framework / platform is one or more wireless communication networks such as cellular, WLAN, and / or one or more dedicated locations such as GPS / GNSS, Satellite Beamon systems. Simultaneously supports positioning of wireless devices in an environment consisting of a specific system.

As described in some embodiments, the unified framework / platform provides all signals outside the wireless device and / or specific (dedicated) network elements (WLAN AP, eNodeB, LMU, E-SMLC, etc.). Performing processing, locating, tracking, and navigation tasks, wireless devices and / or network elements are responsible for collecting and preprocessing snapshots of signals used for locating, tracking, and navigating wireless devices.

As described in some embodiments, signal processing, location identification, tracking, and navigation are all performed within the LSU, and preprocessing of signal snapshots involves sending snapshot data to the LSU, and It includes optionally performing a compression operation to reduce the amount of snapshot data sent to the LSU.

As described in some embodiments herein, the LSUs are:
1. 1. It may be deployed inside the network infrastructure or the operator's IP service network.
2. It may be deployed on a server in a cloud computing-based centralized radio access network (C-RAN) baseband processing edge facility and integrated as a hosted application.
3. 3. It may be hosted within the computing cloud of the network and / or the service network cloud of the operator via a secure remote internet connection.
4. It may be a fully hosted and managed cloud service connected to one or more operators' network infrastructure and / or IP service network via a secure remote internet connection.
5. It may be hosted by a stand-alone private network such as a CBRS, a Selected Access Access network, or a WLAN network.
The latter three embodiments support LaaS data delivery.

In connection with wireless networks, downlink communication is a wireless device that always receives signals from nodes in one or more networks (cell base stations, WLAN APs, satellites, etc.). At the same time, uplink communication always receives signals transmitted from / by the wireless device to one or more network nodes (cell base stations, WLAN APs, etc.) and / or dedicated infrastructure elements (LMUs). Is. The process of locating a wireless device using a downlink signal is called downlink positioning. Similarly, the positioning process using the uplink signal is called uplink positioning.

The signals used to locate wireless devices in connection with the positioning of a particular network (system) are typically either downlink or uplink, such as GPS / GNSS, Terrestrial Beacon systems, etc. be.

As described in some embodiments herein, the unified framework / platform implements:
1. 1. Reduce heavy computational load from wireless devices and / or specific (dedicated) network elements.
2. Enable LaaS for current and future network architectures / environments.
3. 3. Combined uplink / downlink or downlink / uplink that improves the reliability and positioning accuracy of locating systems, such as advanced features that were previously impossible (using existing architectures). Enables positioning and so on.
4. It provides scalable (virtually unlimited) computational bandwidth and resources to support new advanced locating technologies.
5. Reduce the logistical load associated with multi-network management and interfaces, new technology upgrades, and HW / SW legacy constraints.
6. It enables machine learning and improves the reliability of position confirmation and the accuracy of position determination.
7. The wireless device positioning process can be run continuously in the background.
8. Utilize historical data and crowdsourcing.
Therefore, ubiquitous high-precision positioning that provides the latest position information with low latency and without adversely affecting the power consumption of the wireless device becomes possible.

As described in some embodiments, the LSU is one or more of signal processors operating on snapshot data from one or more wireless devices and / or one or more network specific (dedicated) elements. May include.

As described in some embodiments, one or more signal processors in the LSU operate on downlink and / or uplink snapshot data.

As described in some embodiments, the LSU also includes a navigation processor (also known as a positioning engine), which receives output from one or more signal processors and numerous positioning methods. Perform position estimation and position tracking calculations by using a combination of / techniques / techniques, eg, multiple methods, techniques, and techniques, ie hybrid positioning.

As described in some embodiments, the LSU signal processor and positioning engine also receive auxiliary / assist information.

As described in some embodiments, the LSU signal processor may be part of a positioning engine.

As described in some embodiments, the LSU is configured for signaling and information exchange with wireless devices and / or specific (dedicated) elements of one or more networks and networks within one or more networks. It may include a communication processor. For example, signaling may be in accordance with the OMA SUPL protocol and / or the 3GPP LPP / LPPa protocol, MTTQ, and the like.

Further features and advantages of the embodiments are described in the following description, some of which may be apparent from the description or may be learned by implementing the embodiments. The advantages of the embodiments will be realized and achieved by the description and claims herein, as well as the structures specifically pointed out in the accompanying drawings.

It should be understood that both the above overview and the following detailed description are exemplary and purposeful and are intended to provide a further description of the claimed embodiment.

The accompanying drawings are included to provide a better understanding of the embodiments, are incorporated herein by reference, and form part thereof, but such drawings show some embodiments and are viewed in conjunction with the description. , Will help explain the principles of the embodiments.

The frequency component of the narrow band ranging signal according to one embodiment is shown. The frequency component of the narrow band ranging signal according to one embodiment is shown. The frequency components of an exemplary broadband ranging signal are shown. A block diagram of a master unit and a slave unit of an RF movement tracking and locating system according to an embodiment is shown. A block diagram of a master unit and a slave unit of an RF movement tracking and locating system according to an embodiment is shown. A block diagram of a master unit and a slave unit of an RF movement tracking and locating system according to an embodiment is shown. An embodiment of a synthesized wideband baseband ranging signal is shown. The erasure of the preceding portion of the signal by cancellation according to one embodiment is shown. It shows the cancellation of the preceding part with less carrier according to one embodiment. An embodiment of the phase of the unidirectional transfer function is shown. An embodiment of the position identification method is shown. The LTE reference signal mapping is shown. An embodiment of the extended cell ID + RTT positioning technique is shown. An embodiment of the OTDOA positioning technique is shown. The operation of the time observation unit (TMO) installed in the eNB facility of the operator according to one embodiment is shown. An embodiment of the figure of a wireless network locating device is shown. An embodiment of a wireless network relocation downlink ecosystem for enterprise applications is shown. An embodiment of a wireless network relocation downlink ecosystem for network wide applications is shown. An embodiment of a wireless network relocation uplink ecosystem for enterprise applications is shown. An embodiment of a wireless network location uplink ecosystem for network wide applications is shown. Demonstrates an embodiment of a UL-TDOA environment that may include one or more DAS and / or femto / small cell antennas. FIG. 18 shows an embodiment of a UL-TDOA as shown in FIG. 18, which may include one or more cell towers that can be used in place of a DAS base station and / or a femto / small cell. An embodiment of cell-level position identification is shown. An embodiment of specifying the position of a serving cell and a sector ID is shown. An embodiment of E-CID + AoA position identification is shown. An embodiment of AoA position identification is shown. An embodiment of a TDOA having a wide distance and a close distance between receiving antennas is shown. An embodiment of a three-sector deployment is shown. An embodiment of antenna port mapping is shown. An embodiment of the U-TDOA positioning technique of LTE Release 11 is shown. An embodiment of a high-level block diagram of a multi-channel position management unit (LMU) is shown. An embodiment of the DL-OTDOA technique in a wireless / cellular network having a location server is shown. An embodiment of the U-TDOA technique in a wireless / cellular network having a location server is shown. An embodiment of the description of a rack mount enclosure is shown. An embodiment of a high-level block diagram of a plurality of single-channel LMUs clustered (integrated) within a rack-mounted enclosure is shown. An embodiment of a high-level block diagram of a plurality of small cells having an integrated (one-to-one antenna connection / mapping) LMU clustered (integrated) within a rack-mounted enclosure is shown. An embodiment of a high level block diagram of the integration of LMU and DAS is shown. An embodiment of a high-level block diagram of the integration of LMU and WiFi infrastructure is shown. An embodiment of a depiction of UE positioning in a "two tower" environment is shown. Indicates timing advance adjustment. The transmission / reception timing difference between the eNB and the UE is shown. The architecture for UE positioning in E-UTRAN is shown. An embodiment of a wireless network system architecture for UE positioning is shown. An arbitrary embodiment of a wireless network system architecture for UE positioning is shown. Shows the current multi-network / multiple types of access node environment. The current LTE EPC architecture is shown. A direct connection of an embodiment of LSU is shown. The connection of the LSU embodiment via the IP service network is shown. Shows E-SMLC with a relay entity. An embodiment of a unified framework / platform is shown.

Here, an embodiment of the present embodiment will be referred to in detail, and an example thereof will be shown in the accompanying drawings.

The present embodiment relates to methods and systems for identifying, tracking, and locating RF-based objects, such as RTLS. According to one embodiment, the method and system use narrowband ranging signals. Although this embodiment operates in the VHF band, it can also be used in the HF band, LF band, and VLF band, as well as the UHF band and higher frequencies. It uses a multipath mitigation processor. The use of a multipath mitigation processor increases the accuracy of tracking and locating performed by the system.

The embodiment includes an extremely portable, compact base unit that allows the user to track, locate, and monitor multiple individuals and objects. Each unit has its own ID. Each unit broadcasts an RF signal containing its ID, and each unit can return its ID as well as a feedback signal that can contain voice, data, and additional information. Each unit processes the feedback signal from the other unit and continuously determines their relative and / or actual position depending on the triangulation or trilateration and / or other methods used. .. The present embodiment can also be easily integrated with products such as GPS devices, smartphones, transmission / reception radios, and PDAs. The resulting product will have all the capabilities of a standalone device, leveraging the processing power of existing displays, sensors (eg, altimeters, GPS, accelerometers, and compasses, etc.) and their hosts. .. For example, a GPS device with the device technology described herein would be able to provide the user's location on a map and also map the location of other members of the group.

The size of the embodiment based on the FPGA embodiment is about 2 x 4 x 1 inch to 2 x 2 x 0.5 inch, or smaller as integrated circuit technology improves. Depending on the frequency used, the antenna will be built into the device or project through the device enclosure. An ASIC (application specific integrated circuit) based version of the device will be able to incorporate the functionality of the FPGA and most of the other electronic components into a unit, or tag. An ASIC-based stand-alone version of the product will result in device sizes of 1 x 0.5 x 0.5 inches or less. The size of the antenna is determined by the frequency used and part of the antenna can be incorporated into the enclosure. ASIC-based embodiments are designed to be incorporated into products that may consist solely of chipsets. There should be no significant difference in physical size between master units, or tag units.

The device can use standard system components (off-the-shelf components) that operate in multiple frequency ranges (frequency bands) to handle the multipath mitigation algorithm. Digital signal processing and software defined radio software may be used. Signal processing software combined with minimal hardware allows the construction of radios with transmit and receive waveforms defined by the software.

U.S. Pat. No. 7,561,048 discloses a narrowband ranging signal system in which the narrowband ranging signal is adapted to a low bandwidth channel, for example, using an audio channel that is only a few kilohertz wide. (However, some low-bandwidth channels can extend to tens of kilohertz). This is in contrast to traditional position detection systems that use channels that are hundreds of kilohertz to tens of megahertz wide.

The advantages of this narrowband ranging signal system are as follows. That is, at operating frequencies / frequency bands lower than 1), the ranging signal bandwidth of the conventional position detection system exceeds the carrier (operating) frequency value. Therefore, it is not possible to deploy such a system in other lower frequency bands such as the LF / VLF band and the HF band. Unlike conventional position detection systems, the narrowband ranging signal system described in US Pat. No. 7,561,048 has a ranging signal bandwidth far below the carrier frequency value, so that the LF band, Deployment in the VLF band and other frequency bands can be successful. 2) At the bottom of the RF spectrum (some VLF, LF, HF, and VHF bands, eg UHF bands at maximum), the FCC severely limits the permissible channel bandwidth (12-25 kHz), which is why. Since the conventional ranging signal cannot be used, the conventional position detection system cannot be used. Unlike traditional position detection systems, the ranging signal bandwidth of narrowband ranging signal systems is fully compliant with FCC regulations and other international spectral regulators. 3) It is well known that narrowband signals have an essentially higher signal-to-noise ratio (SNR) than wideband signals (MRI: the basics, by Ray), regardless of the operating frequency / frequency band. H. Hashemi, William G. Bradley ...- 2003). This increases the operating range of the narrowband ranging signal position detection system, regardless of the operating frequency / band, such as the UHF band.

Therefore, unlike conventional position detection systems, narrowband ranging signal position detection systems have less pronounced multipathing at the bottom of the RF spectrum (eg VHF band) and lower frequency bands (at least LF /). It can be deployed in the VLF band). At the same time, the narrowband ranging position detection system can also be deployed in and beyond the UHF band to improve the SNR of the ranging signal and, as a result, increase the operating range of the positioning system.

It is desirable to operate in the VLF band / LF band in order to minimize multipath, eg RF energy reflection. However, at these frequencies, the efficiency of the portable / portable antenna is very poor (less than about 0.1% due to the small antenna length (size) relative to the RF wavelength). In addition, at these low frequencies, noise levels from natural and anthropogenic sources are much higher than at higher frequencies / frequency bands, such as VHF. The combination of these two phenomena can limit the applicability of the position detection system, eg, its operating range and / or portability / portability. Therefore, higher RF frequencies / frequency bands, such as HF, VHF, UHF, and UWB, may be used in certain applications where range of operation and / or portability / portability is very important.

In the VHF and UHF bands, the noise levels from natural and anthropogenic sources are significantly lower than in the VLF, LF, and HF bands, and at VHF and HF frequencies, multipath phenomena (eg, RF). Energy reflection) is less serious than UHF and higher frequencies. Also, in VHF, antenna efficiency is significantly better than in HF and lower frequencies, and in VHF, RF penetration is much higher than in UHF. Therefore, the VHF band provides a good compromise for portable / portable applications. On the other hand, in some special cases, for example, in GPS where the VHF frequency (or lower frequency) cannot penetrate (ie, deflect / refract) the ionosphere, UHF can be a good option. However, in any case (and in all cases / applications), the narrowband ranging signal system will have advantages over conventional wideband ranging signal position detection systems.

The actual application will determine the exact technical specifications (power, radiation, bandwidth, and operating frequency / frequency band, etc.). Narrowband ranging allows users to be licensed, licensed exempt from licensees, or to use the unlicensed frequency bands described in the FCC. This is due to the tightest narrow bandwidth by low frequency band distance measurement (6.25 kHz, 11.25 kHz, 12.5 kHz, 25 kHz, and 50 kHz, which are described in the FCC and comply with the corresponding technical requirements of the appropriate section). This is because it is possible to operate in many different bandwidths / frequencies. As a result, multiple FCC sections and exceptions within such sections are applicable. The major FCC regulations that are applicable are 47 CFR Part 90-Private Land Mobile Radio Services, 47 CFR Part 94 personal Radio Services, 47 CFR Part 15-Radio Frequency in this signal, and 47 CFR Part 15-Radio Frequency. From 100 KHz to 10 to 20 MHz).

Typically, in Part 90 and Part 94, VHF embodiments allow the user to operate the device up to 100 mW under certain exceptions (low power wireless services are an example). For certain applications, the acceptable transmit power in the VHF band is 2-5 watts. At 900 MHz (UHF band), it is 1 W. At frequencies of 160 kHz to 190 kHz (LF band), the permissible transmit power is 1 watt.

Narrowband ranging can accommodate many, if not all, different spectral tolerances and also allows accurate ranging while meeting the most stringent regulatory requirements. This applies not only to FCC, but also to other international organizations that regulate the use of spectra around the world, such as Europe, Japan, and South Korea.

The following is a list of common frequencies used, along with typical power usage and the distance of tags that can communicate with another reader in a real-world environment (IndoadPropage and Wavelength Dan Dobkin). , WJ Communications, V1.4 7/10/02).

The proposed system operates at VHF frequencies and uses proprietary methods for transmitting and processing RF signals. More specifically, DSP techniques and software defined radio (SDR) are used to overcome the constraints of narrowband requirements at VHF frequencies.

By operating at lower (VHF) frequencies, scattering is reduced and penetration is much better. The end result is an increase in the range of approximately 10 times the commonly used frequencies. For example, comparing the measurement range of the prototype with the range of the RFID technology described above is as follows.

Using narrowband ranging techniques, the frequencies commonly used, as well as the range of tags that can communicate with other readers in typical power usage and real-world environments, are significantly increased.

Battery consumption depends on the device design, transmit power, and duty cycle, eg, the time interval between two consecutive distance (position) measurements. In many applications, the duty cycle is large, 10 to 1000 times. For high duty cycle applications, such as 100x, the FPGA version transmitting 100 mW of power will have an uptime of approximately 3 weeks. The ASIC-based version is expected to increase the uptime by a factor of 10. Also, ASICs have essentially lower noise levels. Therefore, the ASIC-based version can also increase the operating range by about 40%.

Those skilled in the art will appreciate that this embodiment significantly increases position detection accuracy in RF difficult environments (eg buildings, urban corridors, etc.) without compromising the long operating range of the system.

Typically, tracking and locating systems use the tracking-locating-navigation method. These methods include arrival time (TOA), arrival time difference (DTOA), and a combination of TOA and DTOA. Time of arrival (TOA) as a distance measurement technique is outlined in US Pat. No. 5,525,967. TOA / DTOA-based systems measure the direct line-of-sight (DLOS) flight time (eg, time delay) of an RF ranging signal and then convert it into a range of distances.

In the case of RF reflection (eg, multipath), multiple copies of the RF ranging signal with different delay times are superimposed on the DLOS RF ranging signal. Tracking-positioning systems that use narrowband ranging signals cannot discriminate between DLOS and reflected signals without mitigating multipath. As a result, these reflected signals cause an error in the DLOS flight time of the estimated ranging signal, which in turn affects the range estimation accuracy.

The present embodiment advantageously uses a multipath mitigation processor to separate DLOS and reflected signals. Therefore, the present embodiment significantly reduces the error in the DLOS flight time of the estimated ranging signal. The proposed multipath mitigation method can be used in all RF bands. It can also be used in broadband ranging signal position detection systems. It can also support a variety of modulation / demodulation techniques, including diffusion spectrum techniques such as DSS (Direct Spread Spectrum) and FH (Frequency Hopping).

In addition, a noise reduction method can be applied to further improve the accuracy of this method. Examples of these noise reduction methods include, but are not limited to, coherent addition, non-coherent addition, matching filtering, and time diversity techniques. The balance of the multipath interference error can be further reduced by applying post-processing techniques such as maximum likelihood estimation (eg, Viterbi algorithm) and minimum variance estimation (Kalman filter).

This embodiment can be used in a system having a simplex operation mode, a half-duplex operation mode, and a full-duplex operation mode. Full-duplex operation is very demanding in terms of complexity, cost, and logistics on RF transceivers, thus limiting the operating range of the system in portable / mobile device embodiments. In half-duplex mode of operation, the reader (often also referred to as the "master") and tag (sometimes referred to as the "slave" or "target") are transmitted by the master or slave at any given time. Controlled by a protocol that only allows you to do that.

Alternating transmission and reception allows a single frequency to be used in distance measurements. Such a configuration reduces the cost and complexity of the system compared to a full-duplex system. The simplex mode of operation is conceptually simpler, but requires tighter synchronization of events between the master unit and the target unit, such as the start of a ranging signal sequence.

In this embodiment, the narrowband ranging signal multipath mitigation processor does not increase the ranging signal bandwidth. This allows the propagation of narrowband ranging signals advantageously using different frequency components. Further ranging signal processing collects synthetic ranging signals in the frequency domain or with a relatively wide bandwidth by statistical algorithms such as super-resolution spectrum estimation algorithms (MUSIC, rootMUSIC, ESPRIT) and / or RELAX. It can be performed in the time domain by applying further processing to the signal. The different frequency components of the narrowband ranging signal may be pseudo-randomly selected, may be continuous or spaced in frequency, and may have uniform and / or non-uniform spacing in frequency.

This embodiment extends the multipath mitigation technique. In the narrowband ranging signal model, the frequency (as introduced elsewhere in this specification) is directly proportional to the range-defined delay + the delay defined by the time delay associated with multipath. It is a complex exponential. This model is independent of signal structure embodiments, such as stepped frequencies, linear frequency modulation, and the like.

The frequency separation between direct path and multipath is nominally very small, and normal frequency domain processing is not sufficient to estimate the direct path range. For example, a stepped frequency ranging signal at a stepping rate of 100 KHz over 5 MHz over a range of 30 meters (delay of 100.07 nanoseconds) results in a frequency of 0.062875 radians / second. A multipath reflection with a path length of 35 meters will result in a frequency of 0.073355. The separation is 0.0104792. The frequency resolution of the 50 sample observables has a native frequency resolution of 0.12566 Hz. Therefore, it is not possible to separate the direct path from the reflected path and accurately estimate the range of the direct path using conventional frequency estimation techniques.

To overcome this limitation, the present embodiment uses a unique combination of a subspace-resolved high-resolution spectral estimation method and an embodiment of multimodal cluster analysis. The subspace decomposition technique relies on dividing the estimated covariance matrix of the observed data into two orthogonal subspaces, the noise subspace and the signal subspace. The theory behind the subspace decomposition method is that the projection of an observable into a noise subspace consists only of noise, and the projection of an observable into a signal subspace consists only of a signal.

The super-resolution spectrum estimation algorithm and the RELAX algorithm can distinguish frequencies (sinusoidal curves) placed in close proximity in the spectrum in the presence of noise. Frequencies need not be associated in harmony, and unlike the Digital Fourier Transform (DFT), the signal model does not introduce any anthropogenicity. For a given bandwidth, these algorithms provide significantly higher resolution than the Fourier transform. Therefore, the direct line of sight (DLOS) can be reliably distinguished from other multipaths (MP) with high accuracy. Similarly, it is possible to apply the thresholding method described later to an artificially generated wider synthetic band ranging signal to reliably distinguish DLOS from other paths with high accuracy. do.

According to this embodiment, digital signal processing (DSP) can be used by a multipath mitigation processor to reliably distinguish DLOS from other MP paths. There are various super-resolution algorithms / techniques in spectrum analysis (spectrum estimation) techniques. Examples include subspace-based methods, i.e., MUSIC Signalization (MUSIC) algorithm or root-MUSIC algorithm, Estimation of Signals Via Rotational Invariance Technology algorithm, Pilot Be done.

The above super-resolution algorithm assumes that the signals colliding with the antenna are not sufficiently correlated. Therefore, performance is significantly degraded in a highly correlated signal environment, as can be encountered in multipath propagation. Multipath mitigation techniques can involve a pretreatment scheme called spatial smoothing. As a result, the multipath mitigation process can be computationally intensive and complex, i.e., increasing the complexity of the implementation of the system. Multipath mitigation with lower system computational cost and complexity of embodiments can be achieved by using the Super Resolution Matrix Bundle (MP) algorithm. MP algorithms are classified as non-search methods. Therefore, it is computationally less complex and eliminates the problems faced by the search methods used in other super-resolution algorithms. In addition, the MP algorithm is not sensitive to correlated signals, requires only a single channel estimate, and can also estimate the delay associated with coherent multipath components.

In all of the above superresolution algorithms, the input (ie, receive) signal is modeled as a complex exponential function of frequency and a linear combination of their complex amplitudes. In the case of multipath, the received signal is as follows.

及びτ_Ｋは、それぞれＫ番目のパスの複素減衰及び伝播遅延である。マルチパス成分は、伝播遅延が昇順で考慮されるようにインデックス付けされる。その結果、このモデルでは、τ_０は、ＤＬＯＳパスの伝播遅延を示す。τ_０値は全てのτ_Ｋのうちの最小値であるため、明らかに最も興味深い値である。位相θ_Ｋは、通常、ある測定周期から別の測定周期までランダムであり、均一の確率密度関数Ｕ（０，２π）を有すると想定される。したがって、本発明者らはα_Ｋ＝ｃｏｎｓｔ（すなわち、一定値）と仮定する。 In the equation, β × e ^{i2πf × t} is a transmission signal, f is an operating frequency, and L is the number of multipath components.

And τ _K are the complex attenuation and propagation delay of the Kth path, respectively. Multipath components are indexed so that propagation delays are considered in ascending order. As a result, in this model, τ ₀ indicates the propagation delay of the DLOS path. The τ ₀ value is clearly the most interesting value because it is the smallest of all τ _K. The phase θ _K is usually assumed to be random from one measurement period to another and have a uniform probability density function U (0,2π). Therefore, _{we assume α K} = const (ie, constant value).

The parameters α _K and τ _K are random time-varying functions that reflect the movement of people and equipment in and around the building. However, since the rate of their variation is very slow compared to the measurement time interval, these parameters can be treated as time-invariant random variables within a given measurement cycle.

All of these parameters are frequency dependent as they are related to radio signal characteristics such as transmission and reflection coefficients. However, in this embodiment, the operating frequency hardly changes. Therefore, it can be assumed that the above parameters are frequency independent.

式中、Ａ（ｆ）は受信信号の複素振幅であり、（２π×τ_Ｋ）は超分解能アルゴリズムによって推定される人工「周波数」であり、動作周波数ｆは独立変数であり、α_ＫはＫ番目のパス振幅である。 Equation (1) can be expressed as follows in the frequency domain.

In the equation, A (f) is the complex amplitude of the received signal, (2π × τ _K ) is the artificial “frequency” estimated by the super-resolution algorithm, the operating frequency f is the independent variable, and α _K is K. The second path amplitude.

In equation (2), the super-resolution estimate of _{(2π × τ K ) and the subsequent τ K} value are based on continuous frequencies. In reality, there are a finite number of measurements. Therefore, the variable f would be a discrete variable rather than a continuous variable. Therefore, the complex amplitude A (f) can be calculated as follows.

は、離散周波数ｆ_ｎにおける離散複素振幅推定値（すなわち、測定値）である。 During the ceremony

Is a discrete complex amplitude estimate (ie, a measured value) at a _{discrete frequency f n.}

は、マルチパスチャネルを通って伝播した後の周波数ｆ_ｎの正弦波信号の振幅及び位相として解釈され得る。超分解能アルゴリズムに基づいた全てのスペクトル推定では、複素数入力データ（すなわち、複素振幅）が必要とされることに留意されたい。 In equation (3),

Can be interpreted as the amplitude and phase of a sinusoidal signal of _{frequency f n} after propagating through a multipath channel. Note that all spectral estimates based on super-resolution algorithms require complex input data (ie, complex amplitudes).

を、複素信号（例えば、分析信号）に変換することが可能である。例えば、このような変換は、ヒルベルト変換又は他の方法を使用することによって達成することができる。しかしながら、短距離の場合、値τ_０は非常に小さく、非常に低い（２π×τ_Ｋ）「周波数」をもたらす。 In some cases, the actual signal data, eg

Can be converted into a complex signal (eg, an analytic signal). For example, such a transformation can be achieved by using the Hilbert transform or other methods. However, for short distances, the value τ ₀ is very small, resulting in a very low (2π × τ _K ) “frequency”.

）のみが使用される場合、推定される周波数の数は、（２π×τ_Ｋ）「周波数」だけでなく、それらの組み合わせも含む。概して、未知の周波数の数を増加させると、超分解能アルゴリズムの精度に影響が及ぶ。したがって、他のマルチパス（ＭＰ）パスからＤＬＯＳパスを確実かつ正確に分離するには、複素振幅推定値が必要である。 These low "frequency" cause problems in embodiments of the Hilbert transform (or other methods). In addition, the amplitude value (eg,

) Is used, the estimated number of _{frequencies includes not only (2π × τ K} ) “frequency” but also combinations thereof. In general, increasing the number of unknown frequencies affects the accuracy of the super-resolution algorithm. Therefore, complex amplitude estimates are needed to reliably and accurately separate the DLOS path from other multipath (MP) paths.

を得るタスク中の、方法及びマルチパス軽減プロセッサの動作の説明である。なお、半二重動作モードを中心に説明しているが、全二重モード向けに容易に拡張され得ることに留意されたい。単信動作モードは半二重モードの一部であるが、追加のイベント同期を必要とする。 The following is the complex amplitude in the presence of multipath

It is a description of the method and the operation of the multipath mitigation processor during the task of obtaining. Although the explanation focuses on the half-duplex mode, it should be noted that it can be easily extended for the full-duplex mode. Simplex mode of operation is part of half-duplex mode, but requires additional event synchronization.

In half-duplex mode of operation, the reader (often also referred to as the "master") and tag (also referred to as the "slave" or "target") only allow the master or slave to transmit at any given time. Controlled by the enabling protocol. In this mode of operation, the tag (target device) acts as a transponder. The tag receives a ranging signal from the reader (master device), stores it in memory, and then retransmits the signal back to the master after a specific time (delay).

Examples of ranging signals are shown in FIGS. 1 and 1A. An exemplary ranging signal uses continuous, different frequency components. Other waveforms, including pseudo-random, separated or orthogonal in frequency and / or time, may also be used as long as the bandwidth of the ranging signal remains narrow. In FIG. 1, the period T _f of each frequency component is long enough to obtain the narrow band characteristics of the ranging signal.

Another modification of the ranging signal having different frequency components is shown in FIG. _{This figure includes multiple frequencies (f 1} , f ₂ , f ₃ , f ₄ , f _n ) transmitted over a long period of time to narrow the individual frequencies. Such signals are more efficient, but occupy a wide band width, and the wide band ranging signals affect the SNR, thereby reducing the operating range. Also, such wideband ranging signals violate FCC requirements for VHF bands or lower frequency bands. However, for certain applications, this wideband ranging signal allows for easier integration into existing signals and transmission protocols. Such signals also reduce tracking-locating time.

These multi-frequency (f ₁ , f ₂ , f ₃ , f ₄ , f _n ) bursts are also continuous and / or pseudo-random, separated or orthogonal in frequency and / or time, and so on. good.

The narrowband ranging mode provides accuracy in the form of instantaneous wideband ranging compared to wideband ranging, while increasing the range in which this accuracy can be achieved. This performance is achieved because, at a constant transmit power, the signal-to-noise ratio (at an appropriate signal bandwidth) in the narrowband ranging signal receiver is greater than the SNR in the wideband ranging signal receiver. The SNR gain is an approximate ratio of the total bandwidth of the wideband ranging signal to the bandwidth of each channel of the narrowband ranging signal. This provides a good trade-off for stationary and slow-moving targets, such as a person walking or running, when very rapid ranging is not required.

The master device and the tag device are the same and can operate in either master mode or transponder mode. All devices include data / remote control communication channels. Devices can exchange information and the master device can remotely control the tag device. In this embodiment shown in FIG. 1, during the operation of the master (ie, the reader), the multipath mitigation processor sends a ranging signal to the tag, and after a certain delay, the master / reader is from the tag. Receives iterative ranging signals.

推定値を決定する。式（３）

は、一方向の測距信号トリップのために定義されることに留意されたい。本実施形態では、測距信号は往復する。換言すれば、測距信号は、マスター／読取機から標的／スレーブへ、及び標的／スレーブからマスター／読取機に戻るという両方向に移動する。したがって、マスターによって受信されて戻される、この往復信号の複素振幅は、以下のように計算され得る。

The master's multipath mitigation processor then compares the received ranging signal with the signal originally transmitted from the master in the form of amplitude and phase of _{each frequency component f n.}

Determine the estimate. Equation (3)

Note that is defined for one-way ranging signal trips. In this embodiment, the ranging signal reciprocates. In other words, the ranging signal travels in both directions, from the master / reader to the target / slave and from the target / slave back to the master / reader. Therefore, the complex amplitude of this round-trip signal received and returned by the master can be calculated as follows.

など複素振幅及び位相値を推定するために使用できる多くの技法が存在する。本実施形態によると、複素振幅の決定は、マスター及び／又はタグ受信機のＲＳＳＩ（受信信号強度インジケータ）値から導出される

値に基づく。位相値

は、読取機／マスターによって受信された、戻ってきたベースバンド測距信号の位相と元の（すなわち、読取機／マスターによって送信された）ベースバンド測距信号の位相とを比較することによって得られる。加えて、マスター及びタグデバイスは独立したクロックシステムを有するため、位相推定誤差に対するクロック精度の影響を分析することによって、デバイス動作の詳細な説明が増補される。上記の説明が示すように、一方向の振幅の

値は、標的／スレーブデバイスから直接得られる。しかしながら、一方向の位相の

値を直接測定することはできない。 For example, matching filtering

There are many techniques that can be used to estimate complex amplitudes and phase values. According to this embodiment, the determination of complex amplitude is derived from the RSSI (received signal strength indicator) value of the master and / or tag receiver.

Based on value. Phase value

Is obtained by comparing the phase of the returned baseband ranging signal received by the reader / master with the phase of the original (ie, transmitted by the reader / master) baseband ranging signal. Be done. In addition, since the master and tag devices have independent clock systems, analyzing the effect of clock accuracy on phase estimation errors supplements the detailed description of device operation. As the above description shows, the amplitude in one direction

The value is obtained directly from the target / slave device. However, in one-way phase

The value cannot be measured directly.

In this embodiment, the ranging baseband signal is the same as the signal shown in FIG. However, for the sake of brevity, in the present specification, the ranging baseband signal consists of only _{two frequency components (F 1} and F 2), each containing a cosine wave or a sine wave of different frequencies with multiple periods. Is assumed. Note that F ₁ = f ₁ and F ₂ = f ₂ . The number of cycles of the first frequency component is L, and the number of cycles of the second frequency component is P. When T _f = constant, each frequency component may have a different number of cycles, so L may or may not be equal to P. Also, there is no time difference between each frequency component, _{and both F 1} and F ₂ start from the initial phase equal to zero.

3A, 3B, and 3C show a block diagram of a master unit or slave unit (tag) of an RF movement tracking and locating system. _FOSC refers to the frequency of the device system clock (crystal oscillator 20 in FIG. 3A). All frequencies generated within the device are generated from this system clock crystal oscillator. The following definitions are used. That is, M is a master device (unit) and AM is a tag (target) device (unit). The tag device operates in transponder mode and is referred to as a transponder (AM) unit.

なお、

であることに留意されたい。 In one embodiment, the device consists of an RF front end and an RF back end, a baseband, and a multipath mitigation processor. The RF backend, baseband, and multipath mitigation processor are implemented within the FPGA 150 (see FIGS. 3B and 3C). System clock generator 20 (see Figure 3A) _oscillates at F OSC = 20 MHz or _{ω OSC = 2π × 20 × 10} 6. This is an ideal frequency because in a real device the system clock frequency is not always equal to 20MHz:

note that,

Please note that.

_{Note that other than the FOSC} frequency of 20 MHz, it can be used without affecting system performance.

The electronic configurations of both units (master and tag) are the same, and different operating modes are programmable in software. The baseband ranging signal is generated in digital form by blocks 155-180 within the master FPGA 150 (see Figure 2B). It consists of two frequency components, each containing a cosine wave or a sine wave of different frequencies with multiple periods. First, at t = 0, the FPGA 150 in the master device (FIG. 3B) outputs a digital baseband ranging signal to its upconverter 50 via the I / Q DACs 120 and 125. FPGA150 begins at _{F 1} frequency, after the time _{T 1,} it starts generating _{F 2} frequency over a period of _{T 2.}

Since the frequency of the crystal oscillator may be different from 20 MHz, the actual frequencies produced by the FPGA will be F ₁ γ ^M and F ₂ γ ^M. Also, time T ₁ will be T ₁ β ^M and T ₂ will be T ₂ β ^M. In IT, T ₁ , T ₂ , F ₁ , and F ₂ are F ₁ γ ^{M *} T ₁ β ^M = F ₁ T ₁ and F ₂ γ ^{M *} T ₂ β ^M = F ₂ T ₂ . It is assumed that both F ₁ T ₁ and F ₂ T _{2 are integers.} This means that _{the initial phases of F 1} and F ₂ are equal to zero.

であり、式中、

は定係数である。同様に、周波数シンセサイザ２５からの出力周波数ＴＸ＿ＬＯ及びＲＸ＿ＬＯ（ミキサ５０及び８５のＬＯ信号）は、定係数によって表され得る。これらの定係数は、マスター（Ｍ）及びトランスポンダ（ＡＭ）についても同一であり、その違いは、各デバイスのシステム水晶発振器２０のクロック周波数にある。 Since all frequencies are generated from the system crystal oscillator 20, the outputs of the master baseband I / Q DACs 120 and 125 are

And during the ceremony,

Is a constant coefficient. Similarly, the output frequencies TX_LO and RX_LO (LO signals of mixers 50 and 85) from the frequency synthesizer 25 can be represented by constant coefficients. These constant coefficients are the same for the master (M) and the transponder (AM), and the difference lies in the clock frequency of the system crystal oscillator 20 of each device.

The master (M) and transponder (AM) operate in half-duplex mode. The RF front end of the master uses an orthogonal upconverter (ie, mixer) 50 to upconvert the baseband ranging signal generated by the multipath mitigation processor and transmit this upconverted signal. After transmitting the baseband signal, the master switches from TX mode to RX mode using the TX / RX switch 15 on the RF front end. The transponder receives a signal using its RF front-end mixer 85 (which produces a first IF) and ADC 140 (which produces a second IF) and downconverts the received signal back.

The second IF signal is then digitally filtered in the transformer's RF backend processor using a digital filter 190, RF backend quadrature mixer 200, digital I / Q filters 210 and 230, digital quadrature oscillator. The 220 and adder 270 are used to further down-convert to the baseband ranging signal. This baseband ranging signal is stored in the memory 170 of the transponder using the RAM data bus controller 195 and the control logic 180.

である。マスターは、トランスポンダの送信を受信し、そのＲＦバックエンドの直交ミキサ２００、デジタルＩフィルタ２１０、デジタルＱフィルタ２３０、デジタル直交発振器２２０（図３Ｃを参照）を使用して、受信した信号をベースバンド信号にダウンコンバートして戻す。 The RF front-end switch 15 is then used to switch the transponder from Rx mode to TX mode and start retransmitting the stored baseband signal after _{a particular delay t RTX.} Note that this delay is measured at the AM (transponder) system clock. therefore,

Is. The master receives the transponder's transmission and uses its RF backend quadrature mixer 200, digital I filter 210, digital Q filter 230, and digital quadrature oscillator 220 (see Figure 3C) to baseband the received signal. Down-convert to a signal and return.

The master then uses the multipath mitigation processor arctan block 250 and phase comparison block 255 to calculate the phase difference between _{F 1} and F _{2 in the received (ie, restored) baseband signal.} .. The amplitude value is derived from RSSI block 240 of the RF backend.

に比例するオーダーであり得、Ｎは振幅値及び位相差値が得られた（すなわち、決定された）ときのインスタンスの数である。 In order to improve the estimation accuracy, it is always desirable to improve the SNR of the amplitude estimate from block 240 and the phase difference estimate from block 255. In one embodiment, the multipath mitigation processor calculates amplitude and phase difference estimates for many time instances over the period of the frequency component of the ranging signal (T _f). These values improve SNR when averaged. Improvement of SNR

Can be on the order proportional to, where N is the number of instances when the amplitude and phase difference values are obtained (ie determined).

形式及び

形式に変換され得る。 Another approach to improving SNR is to determine the amplitude and phase difference values by applying a matched filter technique over a period of time. Yet another approach is to sample the received (ie, repeated) baseband ranging signal frequency component and transmit it in I / Q format over a period of T ≤ T _{f by the} original (ie, master / reader). The phase and amplitude would be estimated by integrating with the frequency component of the baseband ranging signal. The integration has the effect of averaging multiple instances of amplitude and phase in I / Q format. After that, the phase value and amplitude value are calculated from the I / Q format.

Format and

Can be converted to format.

式中、Ｔ_ｆ≧Ｔ_１β^Ｍである。
マスターのＤＡＣ１２０及び１２５の出力における位相は、以下のとおりである。

ＤＡＣ１２０及び１２５は、システムクロックに依存しない内部伝播遅延

を有することに留意されたい。 At t = 0, it is assumed that the master baseband processor (both in FPGA 150) initiates the baseband ranging sequence under the control of the master multipath processor.

In the formula, T _f ≧ T ₁ β ^M.
The phases at the outputs of the master DACs 120 and 125 are as follows.

DACs 120 and 125 have internal propagation delays that do not depend on the system clock.

Please note that it has.

をもたらすであろう。 Similarly, transmitter circuit components 15, 30, 40, and 50 are system clock independent, additional delays.

Will bring.

As a result, the phase of the RF signal transmitted by the master can be calculated as follows.

The RF signal from the master (M) undergoes ^{a phase shift φ MULT} according to the multipath phenomenon between the master and the tag.

The φ ^MULT value depends on the transmission frequency, for example F ₁ and F ₂ . The transponder (AM) receiver cannot resolve each path because the bandwidth of the RF portion of the receiver is limited (ie, narrow). Therefore, when all the reflected signals arrive at the receiving antenna after a certain time, for example, after 1 microsecond (corresponding to a flight of ~ 300 meters), the following equation is applied.

In the AM (transponder) receiver in the first down converter (element 85), the signal phase of the output (eg, the first IF) is as follows.

は、システムクロックに依存しないことに留意されたい。ＲＦフロントエンドのフィルタ及び増幅器（要素９５〜１１０及び１２５）を通過した後、第１のＩＦ信号はＲＦバックエンドのＡＤＣ１４０によってサンプリングされる。ＡＤＣ１４０は、入力信号（例えば、第１のＩＦ）をアンダーサンプリングすると仮定する。したがって、ＡＤＣはまた、第２のＩＦを生成するダウンコンバータのように作用する。第１のＩＦフィルタ、増幅器、及びＡＤＣは、伝播遅延時間を加える。ＡＤＣ出力（第２のＩＦ）においては、以下のとおりである。

Propagation delay in the RF section of the receiver (elements 15 and 60-85)

Note that does not depend on the system clock. After passing through the RF front-end filters and amplifiers (elements 95-110 and 125), the first IF signal is sampled by the RF back-end ADC 140. It is assumed that the ADC 140 undersamples the input signal (eg, the first IF). Therefore, the ADC also acts like a downconverter that produces a second IF. The first IF filter, amplifier, and ADC add propagation delay time. The ADC output (second IF) is as follows.

In the FPGA 150, the second IF signal (from the ADC output) is filtered by the RF backend digital filter 190 and the third downconverter (ie, the quadrature mixer 200, the digital filters 230 and 210, and the digital quadrature oscillator 220). ), Further down-converted to the baseband ranging signal, added back by the adder 270, and stored in the memory 170. In the third down converter output (that is, the orthogonal mixer), it is as follows.

は、システムクロックに依存しないことに留意されたい。 Propagation delay in FIR section 190

Note that does not depend on the system clock.

であることに留意されたい。

After the RX-> TX delay, the stored baseband ranging signal (in memory 170) from the master (M) is retransmitted. RX-> TX delay

Please note that.

By the time the signal from the transponder reaches the receiver antenna of the master (M), the RF signal from the transponder (AM) undergoes ^{another phase shift φ MULT according to multipath.} As mentioned above, this phase shift occurs when all reflected signals arrive at the master's receiver antenna after a certain period of time.

At the master receiver, the signal from the transponder goes through the same process as the down-conversion process at the transponder receiver. The result is a restored baseband ranging signal, which was originally transmitted by the master.
The first frequency component F ₁ is as follows.

The second frequency component F2 is as follows.

式中、Ｔ_{Ｄ＿Ｍ−ＡＭ}は、マスター（Ｍ）及びトランスポンダ（ＡＭ）回路による伝播遅延である。

式中、φ_{ＢＢ＿Ｍ−ＡＭ}（０）は、時間ｔ＝０における、マスター（Ｍ）及びトランスポンダ（ＡＭ）周波数ミキサ（ＡＤＣを含む）からのＬＯ位相シフトである。
また、Ｋ_{ＳＹＮ＿ＴＸ}＝Ｋ_{ＳＹＮ＿ＲＸ＿１}＋Ｋ_ＡＤＣ＋Ｋ_{ＳＹＮ＿ＲＸ＿２}である。 Replace as follows.

In the equation, _{TD_M-AM} is the propagation delay by the master (M) and transponder (AM) circuits.

In the equation, _{φBB_M-AM} (0) is the LO phase shift from the master (M) and transponder (AM) frequency mixers (including ADC) at time t = 0.
Further, K _{SYN_TX} = K _{SYN_RX_1} + K _ADC + K _{SYN_RX_2} .

The first frequency component F1 is as follows.

The first frequency component F1 continues to be as follows.

The second frequency component F2 is as follows.

The second frequency component F2 continues to be as follows.

式中、αは一定である。 Further replace as follows.

In the formula, α is constant.

Next, the formula at the final stage is as follows.

式中、ｉ＝２、３、４．．．．．．．．．．．．．．．であり、２×ΔΦ_{Ｆ１／Ｆｉ}は

に等しい。 From equation (5), it is as follows.

In the formula, i = 2, 3, 4. .. .. .. .. .. .. .. .. .. .. .. .. .. .. And 2 × ΔΦ _{F1 / Fi} is

be equivalent to.

は、以下のとおりである。

For example, the difference between time instances t1 and t2

Is as follows.

式中、Ｔ_ＬＢ＿Ｍ及びＴ_{ＬＢ＿ＡＭ}は、デバイスをループバックモードにすることによって測定される、マスター（Ｍ）及びトランスポンダ（ＡＭ）のＴＸ回路及びＲＸ回路を介した伝播遅延である。マスターデバイス及びトランスポンダデバイスは、Ｔ_ＬＢ＿Ｍ及びＴ_{ＬＢ＿ＡＭ}を自動的に測定することができ、ｔ_ＲＴＸ値も既知であることに留意されたい。 In order to obtain the difference of 2 × ΔΦ _{F1 / F2} , it is necessary to know _{TD_M-AM.}

In the equation, T _{LB_M} and T _{LB_AM} are propagation delays through the TX and RX circuits of the master (M) and transponder (AM) measured by putting the device in loopback mode. Master and the transponder _device, it is possible to automatically measure the _{T LB_M} and _{T LB_AM, t RTX} value also should be noted that it is known.

又は、β^Ｍ＝β^ＡＭ＝１と仮定すると、以下のとおりである。

_{T D_M-AM} is determined from the above equation and the t _RTX value, and therefore, for a given t ₁ and t ₂ , the 2 × ΔΦ _{F1 / F2} value can be determined as follows.

Alternatively, ^{assuming β M} = β ^AM = 1, it is as follows.

From equation (6), it can be concluded that at the operating frequency, the complex amplitude value of the ranging signal can be obtained by processing the returned baseband ranging signal.

は、部分空間アルゴリズムが一定の位相オフセットに対して感受性がないため、ゼロに等しいと仮定することができる。必要に応じて、

値（位相初期値）は、その全体が参照により本明細書に組み込まれる米国特許第７，５６１，０４８号に記載されている狭帯域測距信号法を使用してＴＯＡ（到来時間）を決定することによって求めることができる。この方法は、２×Ｔ_ＦＬＴβ^Ｍに等しい測距信号の往復遅延を推定し、

値は、以下の式から求めることができる。

Initial phase value

Can be assumed to be equal to zero because the subspace algorithm is insensitive to constant phase offsets. as needed,

The value (initial phase value) determines the TOA (time of arrival) using the narrowband ranging signal method described in US Pat. No. 7,561,048, which is incorporated herein by reference in its entirety. Can be obtained by doing. This method estimates the reciprocating delay of the ranging signal equal to 2 × T _FLT β ^{M and}

The value can be obtained from the following formula.

は、マルチパスプロセッサのａｒｃｔａｎブロック２５０によって計算される。ＳＮＲを改善するために、マルチパス軽減プロセッサの位相比較ブロック２５５は、式（６Ａ）を使用して、多くのインスタンスｎ（ｎ＝２、３、４．．．．．．．．．．．．．．．）について

を計算し、次いで、それらを平均してＳＮＲを改善する。なお、２×１０^−６＜ｔ_ｎ＜Ｔ_ｆ＋Ｔ_{Ｄ＿Ｍ−ＡＭ}；ｔ_ｍ＝ｔ_１＋Ｔ_ｆであることに留意されたい。 In one embodiment, the phase value of the returned baseband ranging signal

Is calculated by the arctan block 250 of the multipath processor. To improve the SNR, the phase comparison block 255 of the multipath mitigation processor uses equation (6A) to provide many instances n (n = 2, 3, 4 ... ....)about

Are then averaged to improve the SNR. It should be noted that ^{2 × 10-6} <t _n <T _f + T _{D_M-AM} ; t _m = t ₁ + T _f.

From Equations 5 and 6, it becomes clear that the restored (ie, received) baseband ranging signal has the same frequency as the original baseband signal transmitted by the master. Therefore, despite the fact that the master (M) and transponder (AM) system clocks are different, there is no frequency conversion. Since the baseband signal consists of multiple frequency components, each component consists of a sine wave with multiple periods. Also, the individual component frequencies of the received baseband signal are sampled using the individual component frequencies of the corresponding original (ie, transmitted by the master) baseband signal, and the resulting signal is cycled T≤. It would also be possible to estimate the phase and amplitude of the received ranging signal by integrating over T _f.

を生成する。なお、マスターによって送信された各ベースバンド信号の個々の周波数成分は、Ｔ_{Ｄ＿Ｍ−ＡＭ}によって時間内にシフトされる必要があることに留意されたい。積分動作は、（例えば、ＳＮＲを増加させる）振幅及び位相の複数のインスタンスを平均化する効果をもたらす。位相及び振幅値は、Ｉ／Ｑ形式から、

形式及び

形式に変換され得ることに留意されたい。 This operation is the complex amplitude value of the ranging signal received in I / Q format.

To generate. It should be noted that the individual frequency components of each baseband signal transmitted by the master need to be shifted in time by _{TD_M-AM.} The integrating operation has the effect of averaging multiple instances of amplitude and phase (eg, increasing the SNR). The phase and amplitude values are from the I / Q format.

Format and

Note that it can be converted to a format.

形式及び

形式への変換からなるこの方法は、図３Ｃの位相比較ブロック２５５で実施され得る。したがって、ブロック２５５の設計及び実施形態に応じて、式（５）に基づいた本実施形態の方法、又は本セクションに記載の別の方法のいずれかが使用され得る。 Sampling, integration over period T ≤ T _f , from I / Q format

Format and

This method, which consists of conversion to a format, can be performed in the phase comparison block 255 of FIG. 3C. Therefore, depending on the design and embodiment of block 255, either the method of this embodiment based on formula (5) or another method described in this section may be used.

Although the ranging signal bandwidth is narrow, the frequency difference f _n − f ₁ is relatively large and may be on the order of several megahertz, for example. As a result, the bandwidth of the receiver needs to be kept wide enough for all _{of the f 1} : f _{n ranging signal frequency components to pass through.} The wideband width of this receiver affects SNR. In order to reduce the effective bandwidth of the receiver and improve the SNR, the frequency component of the received distance measurement signal baseband is a digital narrow band adjusted for each individual frequency component of the received baseband distance measurement signal. The filter can be filtered by the RF backend processor in the FPGA 150. However, this large number of digital filters (the number of filters equals the number n of individual frequency components) imposes an additional burden on FPGA resources, increasing cost, size, and power consumption.

In one embodiment, only two narrowband digital filters are used, one filter is always _{tuned for frequency component f 1} and the other filter is tuned for all other frequency components f ₂ : f _n . Can be done. Multiple instances of ranging signals are transmitted by the master. Each instance has two frequencies f ₁ : f ₂ ; f ₁ : f ₃ . .. .. .. .. .. ; _{F i: f} i. .. .. .. .. .. ; F ₁ : consists of only _{f n.} Similar strategies are possible.

Also, by adjusting the frequency synthesizer, for example by _{changing the KSYNC} , it is entirely possible to maintain the baseband ranging signal component in only two (or even one) that produce the rest of the frequency component. Note that it is possible. The LO signals of the upconverter and downconverter mixers are preferably generated using direct digital compositing (DDS) technology. High VHF band frequencies can impose an undesired burden on the transceiver / FPGA hardware. However, at lower frequencies this can be a useful approach. An analog frequency synthesizer can be used, but it may take additional time to stabilize after changing frequencies. Also, in the case of an analog synthesizer, it is necessary to make two measurements at the same frequency in order to cancel the phase offset that may occur after changing the frequency of the analog synthesizer.

_{The actual TD_M-AM} used in the above equation is measured on both the master (M) system clock and the transponder (AM) system clock, for example _{TLB_AM} and t _RTX are counted on the transponder (AM) clock. , T _{LB_M} is counted by the master (M) clock. However, when 2 × ΔΦ _{F1 / F2} are calculated in both, _{TLB_AM} and t _RTX are measured (counted) in the master (M) clock. This causes the following error.

The phase estimation error (7) affects the accuracy. Therefore, it is necessary to minimize this error. If β ^M = β ^AM , in other words, if all master system clocks and transponder (tag) system clocks are synchronized, the contribution from the _{t RTX time is excluded.}

In one embodiment, the master unit (device) and the transponder unit (device) can synchronize the clock with any of the devices. For example, the master device can serve as a reference. Clock synchronization is achieved by using a remote control communication channel, which regulates the frequency of the temperature compensated crystal oscillator TCXO 20 under the control of the FPGA 150. The frequency difference is measured at the output of adder 270 on the master device during transmission of the carrier signal by the selected transponder device.

The master then sends a command to the transponder to increase or decrease the TCXO frequency. Higher accuracy can be achieved by repeating this procedure several times to minimize the frequency at the output of adder 270. Note that in the ideal case the frequency at the output of adder 270 should be equal to zero. Another method is to measure the frequency difference and make an estimated phase correction without adjusting the transponder's TCXO frequency.

β ^{M −} β ^AM can be significantly reduced, but there is a phase estimation error when ^{β M ≠ 1.} In this case, the range of error depends on the long-term stability of the clock generator of the reference device (usually the master (M)). In addition, the process of clock synchronization can take quite a long time (especially when using a large number of units in the field). During the synchronization process, the tracking-location system becomes partially or completely inoperable, adversely affecting system readiness and performance. In this case, the above method that does not require the TCXO frequency adjustment of the transponder is preferable.

Commercially available (off-the-shelf) TCXO components have high accuracy and stability. Specifically, the TCXO components of GPS commercial applications are very accurate. In these devices, the effect of phase error on positioning accuracy can be less than 1 meter and does not require frequent clock synchronization.

を取得した後、更なる処理（すなわち、超分解能アルゴリズムの実行）は、マルチパス軽減プロセッサの一部であるソフトウェアベースの構成要素において実施される。このソフトウェア構成要素は、マスター（読取機）ホストコンピュータのＣＰＵ及び／又はＦＰＧＡ１５０（図示せず）に埋め込まれたマイクロプロセッサにおいて実施され得る。一実施形態では、マルチパス軽減アルゴリズムのソフトウェア構成要素は、マスターホストコンピュータのＣＰＵによって実行される。 Narrowband ranging signal Multipath mitigation processor returns complex amplitude of narrowband ranging signal

Further processing (ie, execution of the super-resolution algorithm) is performed on the software-based components that are part of the multipath mitigation processor. This software component can be implemented in a microprocessor embedded in the CPU and / or FPGA 150 (not shown) of a master (reader) host computer. In one embodiment, the software components of the multipath mitigation algorithm are executed by the CPU of the master host computer.

The super-resolution algorithm estimates a _{(2π × τ K} ) “frequency”, such as an τ _{K value.} In the final step, the multipath mitigation processor selects the minimum value (ie, DLOS delay time) τ.

In certain cases where the ranging signal narrowband requirement is relaxed to some extent, the DLOS path can be separated from the MP path by using a continuous (in-time) chirp. In one embodiment, this continuous chirp is linear frequency modulation (LFM). However, other chirp waveforms can also be used.

ラジアン／秒のチャープ速度が与えられる。複数のチャープが送信され、受信されて戻される。チャープ信号は、各チャープが同一位相で開始され、デジタル的に生成されることに留意されたい。 Suppose a chirp with a bandwidth of B and a duration of T is transmitted under a multipath mitigation processor. This will

Given a chirp speed of radians / second. Multiple chirps are sent, received and returned. Note that the chirp signal is generated digitally, with each chirp starting in phase.

In a multipath processor, each single received chirp is adjusted so that the returned chirp comes from the center of the area of interest.

であり、式中、ω_０は、０＜ｔ＜Ｔの初期周波数である。
単一の遅延往復τ、例えばマルチパスがない場合、戻された信号（チャープ）は、ｓ（ｔ−τ）である。 The formula for the chirp waveform is

In the equation, ω ₀ is the initial frequency of 0 <t <T.
In the absence of a single delayed round trip τ, eg multipath, the returned signal (chirp) is s (t-τ).

式中、ｅｘｐ（−ｉｗ_０τ_ｋ）は振幅であり、２βτは周波数であり、０≦ｔ≦Ｔである。最後の項は位相であり、ごくわずかであることに留意されたい。 The multipath mitigation processor then "deramps" s (t-τ) by performing a compound conjugate mix with the originally transmitted chirp. The obtained signal is the following complex sine wave.

In the equation, exp (−iw ₀ τ _k ) is the amplitude, 2β τ is the frequency, and 0 ≦ t ≦ T. Note that the last term is the topology, which is negligible.

式中、Ｌは、ＤＬＯＳパスを含む測距信号パスの数であり、０≦ｔ≦Ｔである。 In the case of multipath, the composite degrading signal consists of a plurality of complex sine waves as follows.

In the formula, L is the number of distance measurement signal paths including the DLOS path, and is 0 ≦ t ≦ T.

式中、Ｎはチャープの数であり、

であり、ρ＝Ｔ＋ｔ_ｄｅａｄであり、ｔ_ｄｅａｄは、２つの連続するチャープ間のデッドタイムゾーンであり、２βτ_ｋは、人為的遅延の「周波数」である。ここでも、最も興味深いのは、ＤＬＯＳパス遅延に対応する最低「周波数」である。 Multiple chirps are sent and processed. Each chirp is treated / processed individually as described above. The multipath mitigation processor then aggregates the results of the individual chirp processes as follows:

In the formula, N is the number of chirps,

Is a, is a _{ρ = T + t dead, t} dead is a dead time zone between two consecutive chirp, 2βτ _k is a "frequency" of the artificial delay. Again, the most interesting is the minimum "frequency" that corresponds to the DLOS path delay.

は、以下の時点における複素正弦波の合計のうちのＮ個のサンプルであると考えることができる。

したがって、サンプルの数は、Ｎの倍数、例えばαＮ；α＝１，２，．．．．．．であり得る。 In equation (10),

Can be considered to be N samples of the total of complex sine waves at the following time points.

Therefore, the number of samples is a multiple of N, such as αN; α = 1, 2,. .. .. .. .. .. Can be.

From equation (10), the multipath mitigation processor produces an αN complex amplitude sample in the time domain used in further processing (ie, execution of the superresolution algorithm). This further processing is performed in software components that are part of the multipath mitigation processor. This software component may be executed by a microprocessor (not shown) embedded in the CPU and / or FPGA 150 of the master (reader) host computer, or both. In one embodiment, the multipath mitigation algorithm software is executed by the CPU of the master host computer.

Super-resolution algorithm, estimates of 2Betatau _k "frequency", to produce a like e.g. tau _K value. In the final step, the multipath mitigation processor selects the minimum value, ie, the DLOS delay time τ.

A special processing method called "threshold technique" that can function as an alternative to the super-resolution algorithm will be described. In other words, this method is used to improve reliability and accuracy in distinguishing DLOS paths from other MP paths using artificially generated, broader band ranging signals. NS.

ｓ（ｔ）が周期１／Δｔで周期的であり、任意の整数ｋについてはｓ（ｋ／Δｔ）＝２Ｎ＋１であり、信号のピーク値であることが容易に確認される。式中、図１及び図１Ａにおいてｎ＝Ｎである。 The baseband ranging signal in the frequency domain shown in FIGS. 1 and 1A can be converted into the baseband signal s (t) in the time domain as follows.

It is easily confirmed that s (t) is periodic with a period of 1 / Δt, s (k / Δt) = 2N + 1 for any integer k, and is the peak value of the signal. In the formula, n = N in FIGS. 1 and 1A.

FIG. 4 shows two periods of s (t) when N = 11 and Δf = 250 kHz. This signal appears as a sequence of pulses with a height of 2N + 1 = 23 separated at 1 / Δf = 4 microseconds. Between the pulses is a sinusoidal waveform with variable amplitude and 2N zeros. The wide bandwidth of the signal can be due to the narrowness of the high pulses. It can also be seen that the bandwidth extends from zero frequency to NΔf = 2.75 MHz.

The basic concept of the threshold method used in one embodiment is to improve the reliability and accuracy of artificially generated broad band ranging in synthesis when distinguishing DLOS paths from other MP paths. Is. The threshold method detects when the starting edge of the leading edge of a broadband pulse arrives at the receiver. Due to filtering in the transmitter and receiver, the leading edge does not rise instantly, but pulls out of the noise with a smoothly increasing gradient. Leading edge TOA is measured by detecting when the leading edge exceeds a predetermined threshold T.

A small threshold is desirable because the error delay τ between the true start of the pulse and the point above the threshold is small, which is quickly exceeded. Therefore, if the replica start has a delay greater than τ, then any pulse replica arriving by multipath has no effect. However, the presence of noise limits how small the threshold T can be. One way to reduce the delay τ is to use the derivative of the received pulse instead of the pulse itself (because the derivative rises faster). The quadratic derivative rises even faster. Higher-order derivatives can be used, but in practice thresholded quadratic derivatives are used because the noise level can be raised to unacceptable values.

The 2.75 MHz wide signal shown in FIG. 4 has a fairly wide bandwidth, but is not suitable for range measurement by the above method. This method requires a transmit pulse, each with a leading portion of a zero signal. However, this goal can be achieved by modifying the signal so that the sinusoidal waveform between the pulses is essentially canceled. In one embodiment, it is done by constructing a waveform that closely approximates the signal at selected intervals between high pulses, and then subtracting the constructed waveform from the original signal.

This technique can be demonstrated by applying it to the signal of FIG. The two black dots shown on the waveform are the endpoints at interval I, centered between the first two pulses. The left and right endpoints of interval I, which were experimentally determined to provide the best results, are:

ｈ（ｔ）は、以下のように合計によって生成される。

式中、

であり、係数α_ｋは、間隔Ｉにわたって最小二乗誤差を最小化するように選択される。

Attempts are made to generate a function g (t) that essentially cancels the signal s (t) at this interval but is less harmful outside the interval. Equation (11) first shows that s (t) is a sine wave sinπ (2N + 1) Δft modulated by 1 / sinπΔft, so that first, a function h (t) that closely approximates 1 / sinπΔft at intervals I. ) Is found, and as a product thereof, g (t) is formed as follows.

h (t) is generated by summing as follows.

During the ceremony

The coefficient α _k is selected to minimize the least squares error over the interval I.

The solution can be easily obtained by incorporating the partial derivative of J with respect to _{α k and setting it equal to zero.} As a result, a linear system of M + 1 equation is obtained as follows.

This _{can be solved for α k} and is as follows in the equation.

next,

Using the definition of the _{function φ k} (t) obtained by (12), it is as follows.

式中、以下のとおりである。

Subtract g (t) from s (t) to obtain a function r (t) that should essentially cancel s (t) at interval I. As shown in the annex, it is suitable to select M = 2N + 1 as the upper limit M of the total in the formula (20). The results of this value and the attached documents are as follows.

In the formula, it is as follows.

From equation (17), it can be seen that a total of 2N + 3 frequencies (including the zero frequency DC term) is required to obtain the desired signal r (t). FIG. 5 shows the signal r (t) obtained for the original signal s (t) shown in FIG. 1, where N = 11 in the equation. In this case, 25 carrier waves ( _{including DC term b 0} ) are required to construct r (t).

The important properties of r (t) constructed as described above are as follows.

1. 1. As can be seen from (14), the lowest frequency is zero Hz and the highest frequency is (2N + 1) ΔfHz. Therefore, the total bandwidth is (2N + 1) ΔfrHz.

に位置する正弦関数である１つの搬送波を除いて、Δｆ間隔の余弦関数（ＤＣを含む）である。 2. All carriers have frequencies

It is a cosine function (including DC) with a Δf interval, except for one carrier wave which is a sine function located at.

3. 3. The original signal s (t) has a period 1 / Δf, while r (t) has a period 2 / Δf. The first half of each cycle of r (t) (which is the entire cycle of s (t)) includes a signal canceling portion, and the second half r (t) is a portion that vibrates significantly. Therefore, the cancellation of the preceding portion occurs only every other cycle of s (t).

This is because the cancel function g (t) actually enhances s (t) every other cycle of s (t). The reason is that g (t) reverses its polarity at each peak of s (t), but s (t) does not. A method of including a cancel portion that increases the processing gain by 3 dB for each cycle of s (t) will be described below.

4. The length of the canceled portion of s (t) is about 80 to 90% of 1 / Δf. Therefore, Δf needs to be small enough to be long enough to remove all residual signals from the previous non-zero portion of r (t) due to multipath.

5. Immediately after each zero portion of r (t) is the first period of the vibrating portion. In one embodiment, in the TOA measurement method described above, the first half of this cycle is used to measure the start of TOA, specifically its rise. It should be noted that the peak value of this first half period (called the main peak) is slightly larger than the corresponding s (t) peak located at about the same time point. The width of the first half cycle is approximately inversely proportional to NΔf.

6. A large amount of processing gain can be achieved by doing the following:

(A) The signal r (t) is used repeatedly. This is because r (t) is periodic with a period of 2 / Δf. Further, a processing gain of 3 dB can be further obtained by the method described later.

(B) Narrowband filtering. Since each of the 2N + 3 carriers is a narrowband signal, the occupied bandwidth of the signal is much smaller than the bandwidth of the wideband signal over the entire allocated frequency band.

In the case of the signal r (t) shown in FIG. 5, where N = 11 and Δf = 250 kHz, the length of the canceled portion of s (t) is about 3.7 microseconds, that is, 1,110 meters. This is more than sufficient to remove all residual signals from the previous nonzero portion of r (t) due to multipath. The main peak has a value of approximately 35 and the maximum magnitude in the preceding portion (ie, canceled) region is about 0.02, which is 65 dB smaller than the main peak. This is desirable to obtain good performance using the TOA measurement threshold technique as described above.

マイクロ秒である。この例は、単位時間当たりでより多くの周期を有するため、より多くの処理利得の達成が期待され得る。 The use of fewer carriers is shown in FIG. This figure shows the signals generated using Δf = 850 kHz, N = 3, and M = 2N + 1 = 7 for a total of only 2N + 3 = 9 carriers. In this case, the signal period is slightly smaller than that of the signal of FIG. 5, which has a period of 8 microseconds.

Microseconds. Since this example has more cycles per unit time, more processing gain can be expected to be achieved.

However, because fewer carriers are used, the amplitude of the main peak is about one-third as large as before and tends to cancel the expected additional processing gain. Also, the length of the zero signal leading portion is shorter, about 0.8 microseconds, or 240 meters. This should still be sufficient to remove all residual signals from the previous nonzero portion of r (t) due to multipath. The total bandwidth of (2N + 1) Δf = 5.95 MHz is almost the same as before, and the width of the half cycle of the main peak is also almost the same. Since fewer carriers are used, there should be some additional processing gain if each carrier is narrowband filtered at the receiver. Moreover, the maximum magnitude in the preceding portion (ie, canceled) region is here about 75 dB lower than the main peak, an improvement of 10 dB from the previous example.

Transmission at RF Frequency: Up to this point, r (t) has been described as a baseband signal for brevity. However, it can be converted to RF, transmitted, received, and then reconfigured as a baseband signal at the receiver. To illustrate, the baseband signal r (t) propagates through one of the multipath propagation paths having the subscript j (radians / arcs are used to simplify the notation). Consider what happens to one of the frequency components ω _{k of.}

Here, it is assumed that the transmitter and the receiver are frequency-synchronized. The parameter b _k is the k-th coefficient in the equation (21) with respect to r (t). The parameters τ _j and φ _j are the path delay and phase shift of the jth propagation path (due to the dielectric properties of the reflector). The parameter θ is the phase shift that occurs during down-conversion to baseband at the receiver. A similar series of functions can be presented for the sine component of equation (21).

Note that the final baseband signal in equation (20) still has a leading portion of the zero signal, as long as the leading portion of the zero signal at r (t) has a length sufficiently greater than the maximum significant propagation delay. It is important to. Naturally, when all frequency components (subscript k) over all paths (subscript j) are combined, the baseband signal at the receiver is r (t) distorted, including all phase shifts. It becomes a version.

Continuous carrier transmission and signal reconstruction are shown in FIGS. 1 and 1A. In the transmitter and receiver, it is assumed that time synchronization and frequency synchronization are performed, and the 2N + 3 transmitted carriers do not need to be transmitted at the same time. As an example, consider transmitting a signal whose baseband representation is that of FIGS. 1A and 6.

In FIG. 6, it is assumed that N = 3 and each of the nine frequency components of 1 millisecond is transmitted sequentially. The start and end times of each frequency transmission are known to the receiver, so that reception of each frequency component at each time can be started and ended in sequence. Since the signal propagation time is very short compared to 1 millisecond (usually less than a few microseconds for intended use), only a small portion of each reception frequency component should be ignored and received. The machine can be easily erased.

の主ピークから使用可能な更なる処理利得が存在する。 The entire process of receiving the nine frequency components can be repeated with additional reception of 9 ms blocks to increase the processing gain. With a total receive time of 1 second, there are about 111 such 9 ms blocks that can be used for processing gain. Furthermore, in each block,

There is additional processing gain available from the main peak of.

In general, it is worth noting that signal reconstruction can be very economical and essentially allows all possible processing gains. For each 2N + 3 reception frequencies, do the following:
1. 1. The phase and amplitude of each 1 millisecond reception of the frequency is measured to form a series of stored vectors (phasors) corresponding to the frequency.
2. The stored vector of the frequency is averaged.
3. 3. Finally, 2N + 3 vector means of 2N + 3 frequencies are used to reconstruct one cycle of the baseband signal with period 2 / Δf, and the reconstruction is used to estimate the TOA of the signal.

This method is not limited to 1 millisecond transmission, and the length of transmission can be increased or decreased. However, the total time of all transmissions should be short enough to freeze all operations of the receiver or transmitter.

Cancellation of r (t) in another half cycle: Cancellation between peaks of s (t) is possible by simply reversing the polarity of the cancel function g (t) (r (t) was previously It was vibrating). However, in order to cancel between all peaks of s (t), it is necessary to apply a version of the function g (t) and its polarity inverted in the receiver, which involves weighting the coefficients in the receiver. ..

Coefficient weighting in the receiver: If desired, the coefficient b _k of equation (21) may be used in the construction of r (t) in the transmitter and instead introduced in the receiver. This, in Formula (20) final signal is the same when b _k is introduced in the final step rather than at the start, it can be readily seen by considering a series of signals. Ignoring the noise, the values are:

The transmitter can then transmit all frequencies with the same amplitude, simplifying its design. It should also be noted that this method is noise weighted at each frequency and its effect should be taken into account. It should also be noted that the coefficient weighting should be done at the receiver in order to affect the polarity reversal of g (t) and obtain twice the available main peaks.

Scaling of Δf to the center frequency in the channel: Channel-formed transmissions with constant channel spacing are required to meet the FCC requirements at VHF or lower frequencies. For channel-formed transmission bands (for VHF and lower frequency bands) with constant channel spacing, which are smaller than the total allocated band, make some adjustments to Δf as needed. This allows all transmit frequencies to be centered on the channel without significant performance changes from the original design values. In the two examples of baseband signals presented earlier, all frequency components are multiples of Δf / 2, so if the channel spacing divides Δf / 2, the lowest RF transmission frequency is concentrated in one channel. However, all other frequencies can fit in the center of the channel.

In some radio frequency (RF) -based identification, tracking, and locating systems, both the master unit and the tag unit perform voice, data, and control communication functions in addition to performing distance measurement functions. do. Similarly, in one embodiment, both the master unit and the tag perform voice, data, and control communication functions in addition to distance measurement functions.

According to one embodiment, ranging signals are subject to a wide range of advanced signal processing techniques such as multipath mitigation. However, these techniques may not be suitable in their own right for voice, data, and control signals. As a result, the operating range of the proposed system (and other existing systems) is outside the range during voice and / or data and / or control communication, not by the ability to measure distance reliably and accurately. Can be limited by

In other radio frequency (RF) -based identification, tracking, and locating systems, distance measurement functions are separated from voice, data, and control communication functions. In these systems, separate RF transceivers are used to perform voice, data, and control communication functions. The disadvantage of this approach is that it increases the cost, complexity, size, etc. of the system.

To avoid the above drawbacks, in one embodiment, some individual frequency components of the narrowband ranging signal or baseband narrowband ranging signal are modulated with the same data / control signal, in the case of voice. Is modulated with digital voice packet data. At the receiver, the individual frequency components with the highest signal strength are demodulated, and the reliability of the information obtained can be further improved by performing "voting" or other signal processing techniques with information redundancy. can.

By this method, the "null" phenomenon can be avoided and the incoming RF signals from multiple paths are destructively coupled with the DLOS path and with each other, thus significantly reducing the received signal strength, which is referred to as the SNR. Associate. Further, by such a method, it is possible to find a set of frequencies in which incoming signals from multiple paths are destructively coupled to and from the DLOS path, thus increasing the received signal strength and associating it with the SNR.

As mentioned above, spectrum estimation-based superresolution algorithms generally use the same model (complex exponential functions of frequencies and linear combinations of their complex amplitudes). This complex amplitude is given by Equation 3 above.

All spectrum estimation-based super-resolution algorithms need to know in advance the number of complex exponential functions, that is, the number of multipath passes. This number of complex exponential functions is called the model size and is determined by the number of multipath components L as shown in Equations 1-3. However, this information is not available when estimating path delays (for RF tracking-location applications). Therefore, another dimension, that is, model size estimation, is added to the spectrum estimation process by the super-resolution algorithm.

It has been shown that if the model size is underestimated, the accuracy of frequency estimation is affected, and if the model size is overestimated, the algorithm produces pseudo frequencies, eg, frequencies that do not exist. (Kei Sakaguchi et al., Environment of the Model Order Algorithm Error in the ESPRIT Based High Resolution Techniques). Existing methods of model size estimation, such as AIC (Akaike's Information Criterion) and MDL (Minimum Description Length), are highly sensitive to the correlation between signals (complex exponential function). However, this is always the case for RF multipath. For example, the correlation will always remain, even after the application of the anteroposterior smoothing algorithm.

In Sakaguchi's paper, an overestimation model is used to estimate these signal powers (amplitudes), estimate these signal powers (amplitudes), and then mock by removing very low power signals. It has been proposed to discriminate the actual frequency (signal) from the frequency (signal). This method is an improvement over existing methods, but is not guaranteed. We carried out the method of Kei Sakaguchi et al. And performed simulations for more complex cases with larger model sizes. In some cases, it was observed that the pseudo signal could have an amplitude very close to the actual signal amplitude.

All spectrum estimation-based super-resolution algorithms work by dividing the complex amplitude data of the incoming signal into two subspaces, the noise subspace and the signal subspace. When these subspaces are properly defined (separated), the model size is equal to the signal subspace size (dimensions).

In one embodiment, model size estimates are achieved using "F" statistics. For example, in the Esprit algorithm, the singular value decomposition (with forward / backward correlation smoothing) of the estimates of the covariance matrix is ordered in ascending order. After that, division is performed by dividing the (n + 1) eigenvalue by the nth eigenvalue. This ratio is an "F" random variable. In the worst case, it is (1,1) a random variable with "F" degrees of freedom. (1,1) The 95% confidence interval for the "F" random variable with degrees of freedom is 161. By setting this value as a threshold value, the model size is determined. Also note that for noise subspaces, the eigenvalues represent an estimate of the noise power.

This method of applying "F" statistics to the ratio of eigenvalues is a more accurate model size estimation method. Note that other degrees of freedom in the "F" statistic can be used in threshold calculations and, as a result, in model size estimation.

Nevertheless, in some cases, two or more very close (temporal) signals can return to one signal due to a flaw in real-world measurements. As a result, the above method will underestimate the number of signals, i.e. the model size. It is advisable to increase the model size by adding a certain number, as underestimating the model size reduces the accuracy of frequency estimation. This number can be determined experimentally and / or from simulation. However, if the signals are not close together, the model size will be overestimated.

In such a case, a pseudo, that is, a frequency that does not exist may appear. As mentioned above, in some cases it has been observed that the pseudo signal has an amplitude very close to the actual signal, so the use of signal amplitude for pseudo signal detection does not always work. Therefore, in addition to the amplitude discrimination, a filter can be implemented to improve the probability of removing the pseudo-frequency.

The frequency estimated by the super-resolution algorithm is an artificial frequency (Equation 2). In practice, these frequencies are the individual path delays in a multipath environment. As a result, there should be no negative frequencies, and all negative frequencies produced by the super-resolution algorithm are pseudo-frequency to be rejected.

値から推定することができる。これらの方法はより低い精度を有するが、このアプローチは、遅延、すなわち周波数の区別に使用される範囲を確立する。例えば、

In addition, the DLOS distance range is the complex amplitude obtained during the measurement using a method different from the super-resolution method.

It can be estimated from the value. Although these methods have lower accuracy, this approach establishes the delay, i.e. the range used to distinguish frequencies. for example,

が最大に近い、すなわちゼロを回避するΔｆ間隔における比率は、ＤＬＯＳ遅延範囲を提供する。実際のＤＬＯＳ遅延は、最大で２倍大きく、又は小さくあり得るが、これは、疑似結果を拒絶するのに役立つ範囲を規定する。 Signal amplitude

The ratio at the Δf interval where is close to maximum, i.e. avoiding zero, provides a DLOS delay range. The actual DLOS delay can be up to twice as large or small, but this defines a range that helps reject pseudo-results.

In this embodiment, the ranging signal is reciprocating. In other words, the ranging signal travels in both directions, from the master / reader to the target / slave and from the target / slave back to the master / reader.

The master transmits the sound α × e ^−jωt , where ω is the operating frequency in the operating band and α is the sound signal amplitude.

At the target receiver, the received signal (one direction) is as follows.

は、正又は負であり得る。

In the equation, N is the number of signal paths in a multipath environment, K0 and τ ₀ are the amplitude and flight time of the DLOS signal.

Can be positive or negative.

は、周波数領域における一方向マルチパスＲＦチャネルの伝達関数であり、Ａ（ω）≧０である。 During the ceremony

Is the transfer function of the unidirectional multipath RF channel in the frequency domain, and A (ω) ≥ 0.

The target retransmits the received signal as follows.

In the master receiver, the round-trip signal is as follows.

Or

On the other hand, from equations (26) and (28), it is as follows.

は、周波数領域における往復マルチパスＲＦチャネルの伝達関数である。 During the ceremony

Is the transfer function of the round-trip multipath RF channel in the frequency domain.

の式が、τ_０÷τ_Ｎパス遅延に加えて、例えば、τ_０＋τ_１、τ_０＋τ_２．．．．．、τ_１＋τ_２、τ_１＋τ_３、．．．、などこれらのパス遅延の組み合わせを含むためである。 From Equation 29, the round-trip multipath channel has more paths than the one-way channel multipath. this is,

In addition to the τ ₀ ÷ τ _N path delay, for example, τ ₀ + τ ₁ , τ ₀ + τ ₂ . .. .. .. .. , Τ ₁ + τ ₂ , τ ₁ + τ ₃ , ... .. .. , Etc. to include a combination of these path delays.

These combinations dramatically increase the number of signals (complex exponential function). Therefore, the probability of very close (temporal) signals also increases, which can significantly underestimate the model size. Therefore, it is desirable to obtain a one-way multipath RF channel transfer function.

は、標的／スレーブデバイスから直接得ることができる。しかしながら、一方向の位相値

を直接測定することはできない。一方向の位相を、往復位相測定値の観測、つまり、 In one embodiment, the unidirectional amplitude value

Can be obtained directly from the target / slave device. However, the phase value in one direction

Cannot be measured directly. Observation of the reciprocating phase measurement value of the phase in one direction, that is,

から決定することは可能である。

It is possible to decide from.

However, for each value of ω, there are two phase values α (ω), which are as follows.

A detailed description of resolving this ambiguity is given below. When the different frequency components of the ranging signal are close to each other, for the most part, the unidirectional phase can be found by dividing the reciprocating phase by 2. Exceptions include regions close to "zero" and the phase can change significantly even with small frequency steps. Note: In the "zero" phenomenon, incoming RF signals from multiple paths are destructively coupled to and from the DLOS path, thus significantly reducing the received signal strength and associating it with the SNR.

Let h (t) be a one-way impulse response of the communication channel. The corresponding transfer functions in the frequency domain are:

In the equation, A (ω) ≥ 0 is the magnitude and α (ω) is the phase of the transfer function. When a unidirectional impulse response is received and retransmitted and returned over the same channel, the resulting bidirectional transfer function is:

In the formula, B (ω) ≥ 0. It is assumed that the bidirectional transfer function G (ω) is _{known for all ω in some open frequency intervals (ω 1} , ω _2). Is it possible to determine the unidirectional transfer function H (ω) defined by _{(ω 1} , ω ₂ ) that generated G (ω)?

Since the magnitude of the bidirectional transfer function is the square of the magnitude in one direction, the following is clear.

However, the situation is more subtle when trying to restore the phase of the unidirectional transfer function from the observation of G (ω). For each value of ω, there are two phase α (ω) values as follows.

By independently selecting one of two possible phase values for each different frequency ω, many different solutions can be generated.

The following theorem, which assumes that any one-way transfer function is continuous at all frequencies, helps solve this situation.

をＩにおける連続関数とし、式中、β（ω）＝２γ（ω）である。次いで、Ｊ（ω）及び−Ｊ（ω）は、ＩでＧ（ｗ）を生成する一方向伝達関数であり、他には存在しない。 Theorem 1: Let I be an open interval of frequency ω that does not include zero in the bidirectional transfer function G (ω) = B (ω) e ^{jβ (ω).}

Is a continuous function in I, and β (ω) = 2γ (ω) in the equation. Next, J (ω) and −J (ω) are unidirectional transfer functions that generate G (w) at I, and are not present elsewhere.

である。これは、Ｉで弁別可能であり、式中、β（ω）＝２α（ω）である。ＩではＧ（ω）≠０であるため、Ｉでは、Ｈ（ω）及びＪ（ω）はゼロ以外である。したがって、以下のとおりである。 Proof: One of the solutions of the one-way transfer function is a continuous function at I

Is. This can be discriminated by I, and β (ω) = 2α (ω) in the equation. In I, G (ω) ≠ 0, so in I, H (ω) and J (ω) are non-zero. Therefore, it is as follows.

Since H (ω) and J (ω) are continuous at I and non-zero, their ratio is continuous at I, and therefore the right side of (34) is continuous at I. The condition β (ω) = 2α (ω) = 2γ (ω) suggests that for each ω ∈ I, π is either 0 or α (ω) −γ (ω). However, α (ω) -γ (ω) cannot switch between these two values without causing a discontinuity on the right side of (34). Therefore, either α (ω) −γ (ω) = 0 for all ω ∈ I, or α (ω) −γ (ω) = π for all ω ∈ I. In the first case, J (ω) = H (ω) is obtained, and in the second case, J (ω) = H (ω) is obtained.

を作成し、Ｊ（ω）を連続的にするようにβ（ω）＝２γ（ω）を満たすγ（ω）の値を選択することを証明する。この特性、すなわちＨ（ω）を有する解が存在することが知られているため、これを行うことは常に可能である。 This theorem is a function to obtain a one-way solution in any open interval I including the transfer function G (ω) = B (ω) e ^{jβ (ω) other than zero.}

To prove that the value of γ (ω) that satisfies β (ω) = 2γ (ω) is selected so that J (ω) is continuous. It is always possible to do this because it is known that there is a solution with this property, i.e. H (ω).

Another procedure for finding a one-way solution is based on the following theorem.

Theorem 2: Let H (ω) = A (ω) e ^{jα (ω)} be a unidirectional transfer function, and let I be an open interval of frequency ω that does not include zero in H (ω). As a result, the phase function α (ω) of H (ω) must be continuous at I.

であることが分かる。ε＞０は任意に選択されたため、α（ω）→α（ω_０）をω→ω_０と結論付け、したがって位相関数α（ω）はω_０において連続的である。 Proof: Let ω _{0 be} the frequency of interval I. In FIG. 7, the complex value H (ω ₀ ) is drawn as a point in the complex plane by the hypothesis H (ω ₀ ) ≠ 0. Let H (ω ₀ ) be an arbitrary small real number, and consider the angle of the two measured values ε shown in FIG. 7 and the circle tangent to the two half-line OA and OB centered on ε> 0. By assumption, H (ω) is continuous for all ω. Therefore, when ω is sufficiently _{close to ω 0} , the complex value H (ω) is in the circle.

It turns out that. Since ε> 0 was chosen arbitrarily, _{we conclude that α (ω) → α (ω 0} ) is ω → ω ₀ , so the phase function α (ω) is continuous at _{ω 0.}

をＩにおける関数とし、式中、β（ω）＝２γ（ω）及びγ（ω）はＩにおいて連続的である。次いで、Ｊ（ω）及び−Ｊ（ω）は、ＩでＧ（ω）を生成する一方向伝達関数であり、他には存在しない。 Theorem 3: Let I be an open interval of frequency ω that does not include zero in the bidirectional transfer function G (ω) = B (ω) e ^{jβ (ω).}

Is a function in I, and in the equation, β (ω) = 2γ (ω) and γ (ω) are continuous in I. Next, J (ω) and −J (ω) are unidirectional transfer functions that generate G (ω) at I, and are not present elsewhere.

（式中、β（ω）＝２α（ω）である）であることは既知である。ＩではＧ（ω）≠０であるため、Ｉでは、Ｈ（ω）及びＪ（ω）はゼロ以外である。したがって、以下のとおりである。 Proof: The proof is similar to the proof of Theorem 1. One of the solutions of the one-way transfer function is a function

It is known that (in the equation, β (ω) = 2α (ω)). In I, G (ω) ≠ 0, so in I, H (ω) and J (ω) are non-zero. Therefore, it is as follows.

By hypothesis, γ (ω) is continuous in I, and by Theorem 2, α (ω) is also continuous in I. Therefore, α (ω) −γ (ω) is continuous at I. The condition β (ω) = 2α (ω) = 2γ (ω) suggests that for each ω ∈ I, π is either 0 or α (ω) −γ (ω). However, α (ω) -γ (ω) cannot be switched between these two values without being discontinuous at I. Therefore, either α (ω) −γ (ω) = 0 for all ω ∈ I, or α (ω) −γ (ω) = π for all ω ∈ I. In the first case, J (ω) = H (ω) is obtained, and in the second case, J (ω) = −H (ω) is obtained.

を作成し、位相関数γ（ω）を連続的にするようにβ（ω）＝２γ（ω）を満たすγ（ω）の値を選択するだけでよいことを証明する。この特性、すなわちＨ（ω）を有する解が存在することが知られているため、これを行うことは常に可能である。 Theorem 3 is simply a function to obtain a one-way solution in any open interval I containing a non-zero transfer function G (ω) = B (ω) e ^{jβ (ω).}

To prove that it is only necessary to select the value of γ (ω) that satisfies β (ω) = 2γ (ω) so that the phase function γ (ω) is continuous. It is always possible to do this because it is known that there is a solution with this property, i.e. H (ω).

The above theorem shows how to reconstruct the two unidirectional transfer functions that produce the bidirectional function G (ω), but these are only useful in the zero-free frequency interval I of G (ω). In general, G (ω) will be observed in the _{frequency interval (ω 1} , ω _{2) which can contain zero. Below,} (ω _1, ω ₂₎ only zeros finite number of G (omega) is present in, one direction of the transfer function have all of the derivative of the order of (ω _1, ω _2), It is a method that can solve this problem, assuming that all of them are not zero at any given frequency ω.

Suppose (H) ω is a unidirectional function that produces G (ω) _{in the interval (ω 1} , ω ₂ ), and G (ω) has at least one zero in _{(ω 1} , ω _2). Assume. Zero of G (ω) is (ω ₁ , ω ₂ ) with a finite number of adjacent open frequency intervals J ₁ , J ₂ , ... .. .. , Divided into _{J n.} In each of these intervals, the solution (H) ω or − (H) ω is found using either Theory 1 or Theory 3. These solutions need to be "combined", and the combined solution is either (H) ω or-(H) ω for all of _{(ω 1} , ω _2). To do this, two so as not to switch from (H) ω to − (H) ω or from − (H) ω to (H) ω when moving from one segment to the next. We need to know how to pair solutions in adjacent sub-intervals.

Starting from the open subintervals J ₁ and J ₂ are first two adjacent, a summary of the procedure. _{These subsections will abut at frequency ω 1} , which is zero for G (ω) (of course, ω ₁ is not included in any of the subsections). According to the above assumptions about the characteristics of the one-way transfer function, ^{there must be a minimum positive integer n such that H (n)} (ω ₁ ) ≠ 0, and the superscript (n) in the equation. Indicates the nth derivative. Next, according to whether the limit of the n-th derivative of the one-way solution in J ₁ as omega → omega ₁ from the left, the solution in J ₁ is H (omega) or -H (omega), It is either H ⁽ⁿ⁾ (ω ₁ ) or −H ⁽ⁿ⁾ (ω _1). Similarly, the limit of the nth derivative of the unidirectional solution in J ₂ as ω → ω _{1 from the right depends on whether the solution in J 2} is H (ω) or −H (ω). It is either H ⁽ⁿ⁾ (ω ₁ ) or −H ⁽ⁿ⁾ (ω _1). Since H ⁽ⁿ⁾ (ω ₁ ) ≠ 0, the two limit values are when _{the solutions of J 1} and J ₂ are both H (ω) or both are −H (ω) and Only then would it be equal. If extreme values of the left and right are not equal, reversing the solution in subinterval J _2. If not, do not flip.

After inverting the solution in sub-interval J ₂ (if necessary), the same procedure is performed for sub-intervals J ₂ and J ₃ and the solution is inverting in _{sub-interval J 3 (if necessary).} Continuing in this way, finally, a complete solution is constructed in the intervals (ω1, ω2).

Since accurate calculations in the presence of noise are difficult, it is desirable that the higher derivative of H (ω) is not required in the above reconstruction procedure. The problem is that the first derivative of H (ω) is likely to be non-zero at any zero of G (ω), and even if it is not, the second derivative may be non-zero. It is so expensive that this problem is unlikely to occur.

In the actual scheme, the two-way transfer function G (ω) is measured at discrete frequencies, and these frequencies are close enough to make the near zero derivative G (ω) computable fairly accurately. You need to be.

RF-based distance measurements require resolving an unknown number of close, overlapping, and noisy echoes of ranging signals with speculatively known shapes. Assuming that the ranging signal is in a narrow band, in the frequency domain, this RF phenomenon is described as the sum of a large number of sine waves per multipath component, and each with complex attenuation and propagation delay of the path. can do.

When the above Fourier transform is performed, the sum represents this multipath model in the time domain. By exchanging the roles of the time variable and the frequency variable in the representation of this time domain, this multipath model becomes a harmonic signal spectrum in which the propagation delay of the path is converted into a harmonic signal.

Ultra-(high) resolution spectral estimation methods are designed to distinguish dense frequencies within a spectrum and are used to estimate individual frequencies of multiple harmonic signals, eg, path delays. As a result, the path delay can be estimated accurately.

Super-resolution spectral estimation uses the eigenstructure of the covariance matrix of the baseband ranging signal sample and the eigencharacteristics of the covariance sequence to provide a solution to the underlying estimation of individual frequencies, eg, path delays. One of the eigenvalues is that the eigenvalues can be combined and, as a result, divided into orthogonal noise and signal eigenvectors (also known as subspaces). Another intrinsic structural characteristic is the rotation-invariant signal subspace characteristic.

Subspace decomposition techniques (MUSIC, rootMUSIC, ESPRIT, etc.) rely on dividing the estimated covariance matrix of the observed data into two orthogonal subspaces, namely the noise subspace and the signal subspace. The theory behind the subspace decomposition method is that the projection of an observable into a noise subspace consists only of noise, and the projection of an observable into a signal subspace consists only of a signal.

The spectral estimation method assumes that the signal is narrowband and the number of harmonic signals is also known, i.e. it is necessary to know the size of the signal subspace. The size of the signal subspace is called the model size. In general, the details are completely unknown and can change rapidly as the environment changes (especially indoors). One of the most difficult and subtle problems when applying any subspace resolution algorithm is the dimension of the signal subspace, which can be considered as the number of frequency components present, which is for multipath reflection. It is the one with the direct path added. Due to the flaws in the actual measurements, there is always an error in the model size estimation, resulting in poor frequency estimation, i.e. distance accuracy.

To improve the accuracy of distance measurement, one embodiment includes six features that evolve a state-of-the-art method of high resolution estimation of subspace decomposition. It involves combining two or more algorithms that estimate individual frequencies by using different intrinsic structural properties that further reduce the ambiguity of delay path determination.

Root Music minimizes the energy of the projection when the observable is projected into the noise subspace. The Esprit algorithm determines individual frequencies from rotation operators. In many respects, this operation is Music conjugation in that when the observable is projected into the signal subspace, it finds the frequency that maximizes the energy of the projection.

Model size is important for both of these algorithms, and in fact, the model sizes that provide the best performance for Music and Esprit in complex signal environments such as those found in indoor ranging are described below. Because of, they are generally not equal.

For Music, it is preferable to over-identify the basic elements of decomposition as "signal eigenvalues" (type I errors) and fail. This minimizes the amount of signal energy projected into the noise subspace and improves accuracy. For Esprit, the opposite is true. It is preferable to identify the basic elements of decomposition as "noise eigenvalues" and fail. This is also a type I error. This minimizes the effect of noise on the energy projected into the signal subspace. Therefore, the Music model size is generally somewhat larger than the Esprit model size.

Second, in complex signal environments, strong reflections are present and the direct path can be much weaker than some of the multipath reflections, so estimate a sufficiently statistically reliable model size. Sometimes it is difficult. This issue is addressed by estimating the "base" model size for both Music and Esprit and processing the observable data using Music and Esprit within a window of model size defined by the base model size for each. Will be done. As a result, a plurality of measurements are performed for each measurement.

The first feature of this embodiment is the use of F statistics to estimate the model size (see above). The second feature is the use of different type I error probabilities in the Music and Esprit F statistics. This introduces a type I error between Music and Esprit, as described above. A third feature is the use of the base model size and window to maximize the probability of detecting a direct path.

Not all measurements provide a solid answer, as the physical and electronic environments can change rapidly. This is addressed by using cluster analysis with multiple measurements to provide a solid answer. A fourth feature of this embodiment is the use of a plurality of measured values.

Due to the presence of multiple signals, the probability distribution of multiple answers obtained from multiple measurements using multiple model sizes from both Music and Esprit implementations is multimodal. In this application, conventional cluster analysis is not sufficient for this application. A fifth feature is to perform multimodal cluster analysis to estimate the direct and equivalent ranges of the reflected multipath components. A sixth feature is the statistics of the range estimates provided by the cluster analysis (analyzing the range and standard deviation and combining these estimates that are statistically identical, thereby providing a more accurate range. Can be estimated.

The above method can also be used in a broadband ranging signal position detection system.

To derive r (t) in the threshold method, we start with equation (20) and obtain:

が使用される。 Trigonometric formula in the formula

Is used.

However _{, except for α 0} , the coefficient α _k for even k is zero. The reason for this is that in interval I, the function 1 / sinπΔft to be approximated by h (t) is even around the center of I, but the basis function sinkπΔft of even k (k ≠ 0) is of I. Since it is an odd number around the center, it is orthogonal to 1 / sinπΔft at I. Therefore, it can be replaced with k = 2n + 1, and M is a positive odd number. Actually, M = 2N + 1. This choice has been experimentally determined to cancel a significant amount of vibration in section I.

Here, k = N−n is replaced in the first integration, and k = N + n + 1 is replaced in the second integration.

Subtracting g (t) from s (t) gives the following.

Here, it is as follows.

Then, (A4) can be written as follows.

The present embodiment relates to methods for positioning / positioning in wireless communications and other wireless networks that substantially eliminate one or more of the disadvantages of prior art. This embodiment favors the accuracy of tracking and locating functions within multiple types of wireless networks by using the multipath mitigation processes, techniques, and algorithms described in US Pat. No. 7,872,583. To improve. These wireless networks include wireless personal area networks (WPGAN) such as ZigBee and Bluetooth, wireless local area networks (WLAN) such as WiFi and UWB, and typically multiple WLANs, with WiMax being the main example. Examples include metropolitan networks (WMAN), wireless wide area networks (WAN) such as white space TV bands, and mobile device networks (MDN) typically used for voice and data transmission. MDN is typically based on the Global System for Mobile Communications (GSM) and Personal Communications Services (PCS) standards. More recent MDNs are based on the Long Term Evolution (LTE) standard. These wireless networks typically include base stations, desktops, tablets and laptop computers, handsetes, smartphones, actuators, dedicated tags, sensors, and other communication and data devices (generally, all of these devices are "" It consists of a combination of devices such as "wireless network device").

Existing location and positioning information solutions use multiple technologies and networks such as GPS, AGPS, cell phone tower triangulation, and Wi-Fi. Some of the methods used to derive this location information include RF fingerprinting, RSSI, and TDOA. Although acceptable with current E911 requirements, existing locating and ranging methods are required to support future E911 requirements and LBS and / or RTLS application requirements (especially indoor and urban environments). It has no sex and accuracy.

The method described in US Pat. No. 7,872,583 significantly improves the ability to accurately locate and track target devices within a single radio network or a combination of radio networks. This embodiment is an existing tracking and locating method used by wireless networks that use extended cell IDs and OTDOA (observation arrival time difference), such as DL-OTDOA (downlink OTDOA), U-TDOA, UL-TDOA. Is a significant improvement over the embodiment of.

The cell ID positioning technique makes it possible to estimate the position of a user (UE, user equipment) with the accuracy of a particular sector coverage area. Therefore, the achievable accuracy depends on the cell (base station) sectorization scheme and antenna beamwidth. To improve accuracy, the extended cell ID technique adds RTT (Round Trip Time) measurements from the eNB. Note: RTT constitutes the difference between the transmission of a downlink DPCH (dedicated physical channel) (DPDCH) / DPCCH (dedicated physical data channel / dedicated physical control channel) frame and the start of the corresponding uplink physical frame. In this case, the frame acts as a ranging signal. The distance from the eNB can be calculated based on the information on the time it takes for this signal to propagate from the eNB to the UE (see FIG. 10).

In the observation arrival time difference (OTDOA) technique, the arrival time of a signal arriving from an adjacent base station (eNB) is calculated. Upon receiving the signals from the three base stations, the UE location can be estimated within the handset (UE-based method) or within the network (NT-based, UE-based method). The measured signal is CPICH (Common Pilot Channel). Signal propagation time correlates with locally generated replicas. The peak of the correlation indicates the observed propagation time of the measured signal. The arrival time difference between the two base stations determines the hyperbola. At least three reference points are needed to define the two ultrasounds. The position of the UE is at the intersection of these two hyperbolas (see FIG. 11).

Idle Cycle Downlink (IPDL) further improves OTDOA. The OTDOA-IPDL technique is based on the same measurements obtained during the idle cycle as the normal OTDOA time measurements, the serving eNB aborts its transmission and the UE within the coverage of this cell arrives from a distant eNB. To be able to hear the pilot. The serving eNB provides an idle cycle in continuous mode or burst mode. In continuous mode, one idle period is inserted in every downlink physical frame (10 ms). In burst mode, the idle period occurs in a pseudo-random manner. Further improvements are obtained by time-matched IPDL (TA-IPDLL). Time matching produces a common idle cycle, during which each base station either ceases its transmission or transmits a common pilot. The pilot signal measurement is performed during the idle cycle. There are several other techniques that can further enhance the DL OTDOA-IPDL method, such as Cumulative Virtual Blanking, UTDOA (Uplink TDOA), and the like. All of these techniques improve the ability to listen to other (non-serving) eNBs.

One significant drawback of OTDOA-based techniques is that the timing relationships of the base stations need to be known or measured (synchronized) in order for this method to be feasible. In asynchronous UMTS networks, the 3GPP standard proposes a method for restoring this timing. However, network operators do not implement such solutions. As a result, alternatives using RTT measurements instead of CPICH signal measurements have been proposed (see US Patent Application No. 200802855505, John Carlson et al., "SYSTEM AND METHOD FOR NETWORK TIMEING RECORD RECORD IN COMMUNICATIONS").

All of the above methods / techniques are based on measurements of terrestrial signal arrival times and / or arrival time differences (RTT, CPICH, etc.). The problem with such measurements is that they are seriously affected by multipath. This in turn reduces the positioning / tracking accuracy of the methods / techniques described above (see Jakub Marek Borkowski: Performance of Cell ID + RTT Hybrid Positioning Method for UMTS).

Some multipath mitigation techniques use detections / measurements from an excessive number of eNBs or radio base stations (RBSs). The minimum value is 3, but for multipath mitigation, the number of RBSs required is at least 6-8 (“METHOD AND ARRANGEMENT FOR DL-OTDOA (DOWNLINK OBSERVED TIME DIFFERENCE OF ARRIVAL) POSITIONING INA TERM EVOLUTION) WIRELESS COMMUNICATIONS SYSTEM ", International Publication No. 2010/10436). However, the probability of hearing a UE from this large number of eNBs is much lower than that of three eNBs. This is because there are some RBSs (eNBs) that are far from the UE, and the signals received from these RBSs are below the UE receiver sensitivity level or the received signals have a low SNR. be.

In the case of RF reflection (eg, multipath), multiple copies of the RF signal with different delay times are superimposed on the DLOS (direct line of sight) signal. DLOS and reflected signals are suitable because other signals used in various cell IDs and OTDOA methods / techniques such as CPICH, uplink DPCCH / DPDCH, and RTT measurements are signals with limited bandwidth. It cannot be discriminated unless multipath processing / mitigation is performed. Also, without this multipath processing, these reflected signals induce errors in the estimated arrival time difference (TDOA) and arrival time (TOA) measurements, such as RTT measurements.

For example, 3G TS25.515 v. The 3.0.0 (199-10) standard sets the RTT to "... transmit a downlink DPCH frame (signal) and start a corresponding uplink DPCCH / DPDCH frame (signal) from the UE (first). The difference between the reception and the reception of the significant path). This standard does not define what constitutes this "first significant path". The standard continues that "the definition of the first significant path requires further refinement." For example, in a severe multipath environment, situations often occur in which the DLOS signal, which is the first significant path, is significantly attenuated (10 dB to 20 dB) with respect to one or more reflected signals. If the "first significant path" is determined by measuring the signal strength, it can be one of the reflected signals, not the DLOS signal. This results in a decrease in TOA / DTOA / RTT measurements and positioning accuracy, including errors.

In the traditional wireless network generation, locating accuracy has been influenced by the low sampling rates of the frames (signals) used in locating methods, namely RTT signals, CPCIH signals, and other signals. The current third and subsequent generations of wireless networks have much higher sampling rates. As a result, in these networks, the actual effect on positioning accuracy is due to the ground-based RF propagation phenomenon (multipath).

This embodiment can be used in all wireless networks that use reference and / or pilot signals, and / or sync signals, including simplex, half-duplex, and full-duplex modes of operation. For example, the present embodiment operates in a wireless network that uses OFDM modulation and / or derivatives thereof. Therefore, this embodiment operates in the LTE network.

It is also applicable to other wireless networks such as WiMax, WiFi, and white space. Other radio networks that do not use reference and / or pilot or sync signals are one of the following other types of modulation embodiments as described in US Pat. No. 7,872,583. The above may be used: 1) Part of the frame is dedicated to the ranging signal / ranging signal element, as described in US Pat. No. 7,872,583; 2) ranging signal element (US). Patent No. 7,872,583) is embedded in the transmit / receive signal frame; and 3) the ranging signal element (described in US Pat. No. 7,872,583) is embedded in the data. ..

These alternative embodiments use the multipath mitigation processor described in U.S. Patent Application Publication No. 7,872,583, and multipath mitigation techniques / algorithms, in all modes of operation (single, half-duplex, And full double) can be used.

It is also likely that multiple wireless networks will utilize preferred and / or alternative embodiments at the same time. As an example, a smartphone can have Bluetooth, WiFi, GSM, and LTE capabilities and can operate simultaneously on multiple networks. Positioning / location information can be provided using different wireless networks depending on application demand and / or network availability.

The methods and systems of the proposed embodiment utilize wireless network reference / pilot and / or synchronization signals. In addition, reference / pilot / sync signal measurements may be combined with RTT (round trip time) measurements or system timing. According to one embodiment, RF-based tracking and relocation is performed on a 3GPP LTE cellular network, but on other wireless networks using various signaling techniques, such as WiMax, Wi-Fi, LTE, sensors. It can be implemented on a network or the like. Both exemplary above alternative embodiments use the multipath mitigation methods / techniques and algorithms described in US Pat. No. 7,872,583. The proposed system can use digital signal processing by software.

The system of this embodiment utilizes user equipment (UE), such as a mobile phone or smartphone, hardware / software, and base station (node B) / extended base station (eNB) hardware / software. A base station generally consists of a transmitter and a receiver in a cabin or cabinet connected to an antenna by a feeder. These base stations include microcells, picocells, umbrella cells, mobile phone relay towers, routers, and femtocells. As a result, there is little or no additional cost for the UE device and the entire system. At the same time, the positioning accuracy is significantly improved.

The improved accuracy derives from the multipath mitigation provided by this embodiment and US Pat. No. 7,872,583. This embodiment uses a multipath mitigation algorithm, network reference / pilot, and / or synchronization signals, and a network node (eNB). These may be supplemented with RTT (Round Trip Time) measurements. The multipath mitigation algorithm is implemented on the UE and / or the base station (eNB), or both the UE and the eNB.

The present embodiment is a multipath mitigation processor that allows the DLOS signal and the reflected signal to be separated even when the DLOS signal is significantly attenuated (10 dB to 20 dB lower) with respect to one or more reflected signals. Use the algorithm (see US Pat. No. 7,872,583) to your advantage. Therefore, the present embodiment significantly reduces errors in the estimated distance measurement signal DLOS flight time and, as a result, TOA, RTT, and DTOA measurements. The proposed multipath mitigation and DLOS discrimination (recognition) methods can be used on all RF bands and wireless systems / networks. It can also support a variety of modulation / demodulation techniques, including diffusion spectrum techniques such as DSS (Direct Spread Spectrum) and FH (Frequency Hopping).

In addition, a noise reduction method can be applied to further improve the accuracy of this method. Examples of these noise reduction methods include, but are not limited to, coherent addition, non-coherent addition, matching filtering, and time diversity techniques. The remainder of the multipath interference error can be further reduced by applying post-processing techniques such as maximum likelihood estimation (eg, Viterbi algorithm), minimum variance estimation (Kalman filter).

In this embodiment, the multipath mitigation processor and multipath mitigation technique / algorithm do not modify the RTT, CPU, and other signals and / or frames. The present embodiment utilizes wireless network reference, pilot, and / or synchronization signals used to obtain channel responses / estimates. The present invention uses channel estimation statistics generated by the UE and / or eNB (U.S. Patent Application Publication No. 2003/008156 of Iwamatsu et al. (“APPARATUS FOR ESTIMATING PROPAGATION PATH CHARACTERISTICS”), U.S. Pat. No. 7,167,456 (B2). See issue).

The LTE network uses specific (non-data) reference / pilot and / or sync signals (known signals) transmitted in all downlink and uplink subframes and spans the entire cell bandwidth. Sometimes. For the sake of simplicity, the reference / pilot and synchronization signals will be referred to herein as reference signals. An example of LTE reference signals is shown in FIG. 9 (these signals are interspersed between LTE resource elements). From FIG. 9, reference signals (symbols) are transmitted every 6 subcarriers. Moreover, the reference signals (symbols) alternate in both time and frequency. As a whole, the reference signal is intended for each of the three subcarriers.

These reference signals are used for initial cell search by the UE, downlink signal strength measurement, scheduling, handover, and the like. The reference signal includes a UE-specific reference signal used for channel estimation (response determination) for coherent demodulation. In addition to UE-specific reference signals, other reference signals may also be used for channel estimation (see US Patent Application Publication No. 2010/00918286 (A1) by Chen et al.).

LTE uses OFDM (Orthogonal Frequency Division Multiplexing) modulation (technique). In LTE, ISI (intersymbol interference) caused by multipath is handled by inserting a cyclic prefix (CP) at the beginning of each OFDM symbol. The CP provides sufficient delay so that the delayed reflection signal of the previous OFDM symbol disappears before reaching the next OFDM symbol.

The OFDM symbol consists of multiple very close subcarriers. Inside the OFDM symbol, the staggered copy of the current symbol (caused by multipath) results in intercarrier interference (ICI). In LTE, ICI is processed (reduced) by determining the multipath channel response and correcting the channel response in the receiver.

In LTE, the multipath channel response (estimate) is calculated from the subcarrier carrying the reference symbol at the receiver. Interpolation is used to estimate the channel response on the remaining subcarriers. The channel response is calculated (estimated) in the form of channel amplitude and phase. Once the channel response is determined (by periodic transmission of a known reference signal), the channel distortion caused by multipath is mitigated by applying amplitude and phase shifts for each subcarrier (Jim Zylen, Overview of the). 3GPP Long Term Evolution Physical Layer, see white paper).

LTE multipath mitigation is designed to eliminate ISI (by inserting a cyclic prefix), but not to separate the DLOS signal from the reflected signal. For example, a staggered copy of the current symbol temporally spreads each modulated subcarrier signal, thus giving rise to an ICI. Correction of the multipath channel response using the LTE technique described above shortens the modulated subcarrier signal in time, whereas this type of correction is such that the resulting modulated subcarrier signal (inside the OFDM symbol) There is no guarantee that it is a DLOS signal. If the DLOS-modulated subcarrier signal is significantly attenuated with respect to the delayed reflection signal, the resulting output signal is a delayed reflection signal and the DLOS signal is lost.

For LTE compliant receivers, further signal processing includes DFT (Digital Fourier Transform). It is well known that the DFT technique can only resolve (remove) a copy of a signal and / or a time-delayed signal that is longer than the time inversely proportional to the channel bandwidth. The accuracy of this method may be sufficient for efficient data transfer, but not sufficiently accurate for accurate distance measurements in severe multipath environments. For example, to achieve an accuracy of 30 meters, the signal and receiver channel bandwidth should be at least 10 MHz (1/10 MHz = 100 ns). For better accuracy, the signal and receiver channel bandwidth should be wider and 100 MHz per 3 meters.

However, other signals used in OTDOA methods / techniques such as CPICH, uplink DPCCH / DPDCH, various cell ID and RTT measurements, and subcarriers of LTE received signals have bandwidths significantly lower than 10 MHz. As a result, the methods / techniques currently in use (in LTE) will result in positioning errors within a range of 100 meters.

To overcome the above constraints, the present embodiment uses a unique combination of performing a subspace-resolved high-resolution spectral estimation method and performing a multimodal cluster analysis. This analysis and related multipath mitigation methods / techniques and algorithms are described in US Pat. No. 7,872,583, which allows the DLOS path to be reliably and accurately separated from other reflected signal paths.

In a severe multipath environment compared to the methods / techniques used in LTE, this method / technique and algorithm (US Pat. No. 7,872,583) is a DLOS path from another multipath (MP) path. By reliably and accurately separating the above, the accuracy of distance measurement is improved by 20 to 50 times.

The methods / techniques and algorithms described in US Pat. No. 7,872,583 require complex amplitude estimation of ranging signals. Therefore, LTE reference signals used for channel estimation (response determination), as well as other reference signals (such as pilot signals and / or synchronization signals), are also described in US Pat. No. 7,872,583. And can be interpreted as a ranging signal in the algorithm. In this case, the complex amplitude of the ranging signal is the channel response calculated (estimated) by the LTE receiver in the form of amplitude and phase. In other words, the (estimated) channel response statistics calculated by the LTE receiver provide the complex amplitude information required by the methods / techniques and algorithms described in US Pat. No. 7,872,583. be able to.

In an ideal outdoor RF propagation environment that does not include multipath, the phase change of the received signal (distance measurement signal), for example, the channel response phase, is directly proportional to the frequency (straight line) of the signal, and RF in such an environment. The flight time (propagation delay) of a signal can be calculated directly from the phase-to-frequency dependence by calculating the first derivative of the phase-to-frequency dependence. The result is a propagation delay constant.

In this ideal environment, the absolute phase value at the initial (or arbitrary) frequency is not important because the derivative is not affected by the absolute phase value.

In a severe multipath environment, the phase change vs. frequency of the received signal is a complex curve (not a straight line), and the first derivative can be used to accurately separate the DLOS path from other reflected signal paths. Does not provide. This is the reason for using the multipath mitigation processors and methods / techniques and algorithms described in US Pat. No. 7,872,583.

If the phase and frequency synchronization (phase coherency) achieved in a given wireless network / system is very good, then the multipath mitigation processors and methods / techniques described in US Pat. No. 7,872,583 and The algorithm will accurately separate the DLOS path from other reflected signal paths and determine this DLOS path length (flight time).

No additional measurements are required for this phase coherent network / system. In other words, unidirectional distance measurement (single communication distance measurement) can be realized.

However, if the degree of synchronization (phase coherency) achieved in a given wireless network / system is not accurate enough, in a severe multipath environment, the phase and amplitude change vs. frequency of the received signal may be more than one. It can be very similar to measurements made at different positions (distances). This phenomenon can lead to ambiguity in determining the DLOS distance (flight time) of the received signal.

To resolve this ambiguity, it is necessary to know the actual (absolute) phase value of at least one frequency.

However, the amplitude and phase vs. frequency dependence calculated by the LTE receiver does not include the actual phase values, as all amplitudes and phase values are calculated from, for example, the downlink / uplink reference signals relative to each other. .. Therefore, the amplitude and phase of the (estimated) channel response calculated by the LTE receiver requires the actual phase value at at least one frequency (subcarrier frequency).

In LTE, this actual phase value can be determined from one or more RTT and TOA measurements. Or

1) These time stamps for transmitting these signals by the eNB are also known at the receiver (or vice versa), 2) the receiver and the eNB clock are well synchronized in time. And / or 3) it may be determined from the time stamps of one or more received reference signals, provided that the multi-ration technique is used.

All the above methods provide flight time values for one or more reference signals. From the flight time values and frequencies of these reference signals, the actual phase values at one or more frequencies can be calculated.

This embodiment incorporates the multipath mitigation processors, methods / techniques and algorithms described in US Pat. No. 7,872,583: 1) Amplitude and phase vs. frequency calculated by the LTE UE and / or eNB receiver. Dependencies, or 2) Amplitude and phase vs. frequency dependencies calculated by the LTE UE and / or eNB receiver, and a combination with the actual phase values of one or more frequencies obtained by RTT and / or TOA. And and / or in combination with time stamp measurements, achieve highly accurate DLOS distance measurement / positioning in severe multipath environments.

In these cases, the actual phase value is affected by multipath. However, this does not affect the performance of the methods / techniques and algorithms described in US Pat. No. 7,872,583.

With LTE RTT / TOA / TDOA / OTDOA such as DL-OTDOA, U-TDOA, UL-TDOA, measurement can be performed with a resolution of 5 meters. RTT measurements are made during a dedicated connection. Therefore, when the UE is in the handover state and when the UE periodically collects the measured values and returns the report to the UE, a plurality of simultaneous measurements are possible, and the UE is different from the network (base station). DPCH frames are exchanged between them. Like RTT, TOA measurements provide signal flight time (propagation delay), but TOA measurements cannot be made at the same time (Jakub Marek Borkowski: Performance of Cell ID + RTT Hybrid Positioning Method UMTS).

In order to locate the UE on the plane DLOS, it is necessary to measure the distances from / to at least three eNBs. In order to locate the UE in 3D space, it is necessary to measure at least 4 DLOS distances from / to 4 eNBs (assuming at least one eNB is not coplanar).

An example of the UE positioning method is shown in FIG.

If the synchronization is very good, no RTT measurement is needed.

If the degree of synchronization is not accurate enough, methods such as OTDOA, cell ID + RTT and other methods, such as AOA (AO), and their combination with other methods can be used to locate the UE.

The accuracy of the cell ID + RTT tracking-positioning method is affected by the multipath (RTT measurement) and eNB (base station) antenna beamwidth. The antenna beam width of the base station is 33 to 65 degrees. These wide beamwidths result in a positioning error of 50-150 meters in urban areas (Jakub Marek Borkowski: Performance of Cell ID + RTT Hybrid Positioning Method for UMTS). Given that the current LTE RTT distance measurement average error is approximately 100 meters in a severe multipath environment, the typical expected average positioning error currently used in the LTE cell ID + RTT method is approximately 150 meters. Is.

One of the present embodiments is UE positioning based on the AOA method, in which one or more reference signals from the UE are used to locate the UE. This includes the location of the AOA measuring device for measuring DLOS AOA. The device may be juxtaposed with the base station and / or installed in one or more other locations independent of the base station location. The coordinates of these positions appear to be known. No change is required on the UE side.

The device includes a small antenna array and is based on the same multipath mitigation processor, method / technique, and algorithm variants as described in US Pat. No. 7,872,583. This one possible embodiment has the advantage of accurately measuring the AOA of DLOS RF energy from the UE unit (very narrow beamwidth).

In one other option, this additional device is a receive-only device. As a result, its size / weight and cost are very small.

The combination of an embodiment in which an accurate DLOS distance measurement can be obtained and an embodiment in which an accurate DLOS AOA measurement can be performed can significantly improve the accuracy of the cell ID + RTT tracking-positioning method (10 times or more). There will be. Another advantage of this approach is that a single tower can be used to locate the UE at any time (the UE does not need to be in soft handover mode). It is not necessary to synchronize multiple cell towers because accurate positioning can be obtained using a single tower. Another option for measuring DLOS AOA is to use existing eNB antenna arrays and eNB equipment. This option can further reduce the cost of implementing the improved cell ID + RTT method. However, since the eNB antenna is not designed for positioning applications, the positioning accuracy may decrease. In addition, network operators may be reluctant to make necessary changes to base stations (software / hardware).

LTE (Evolved Universal Radio Access (E-UTRA); Physical channels and modulation; 3GPP TS 36.211 Release 9 Technical Reference (Prision), PR, Specialization). These signals are used by the UE for DL OTDA (downlink OTDOA), positioning. This release 9 also requires eNB synchronization. Therefore, the last obstacle of the OTDOA method is eliminated (see paragraph 274 above). PRS improves UE listening ability in UEs of multiple eNBs. Note: Release 9 does not specify eNB synchronization accuracy (some suggestions: 100ns).

U-TDOA / UL-TDOA is in the research stage and will be standardized in 2011.

The DL-OTDOA method (in Release 9) is detailed in US Patent Application Publication No. 2011/01/24347 (A1) ("Method and Apparatus for UE positioning in LTE networks", Chen et al.). Release 9 DL-OTDOA suffers from multipath. Part of the multipath mitigation can be achieved by increasing the PRS signal bandwidth. However, the trade-off is that scheduling complexity increases and the UE position correction interval increases. Moreover, for networks with limited operating bandwidth, eg 10 MHz, the best accuracy is 100 meters (Chen, see Table 1).

The above numbers are the best cases. In other cases, especially when the DLOS signal strength is significantly lower (10 to 20 dB) than the reflected signal, the above positioning / ranging error becomes significantly larger (2 to 4 times).

In the embodiments described herein, distance measurement / positioning is up to 50 times greater than the performance achieved by the DL-OTDOA method of Release 9 and the UL-PRS method of Chen et al. Described in the "Background Techniques" section. Allows for improved accuracy. Therefore, by applying the embodiment of the method described herein to the PRS process of Release 9, the positioning error is reduced to 3 meters or less in 95% of all possible cases. In addition, this accuracy gain reduces scheduling complexity and UE positioning intervals.

In the embodiments described herein, further improvements to the OTDOA method are possible. For example, the distance to a serving cell can be determined from the signals of other serving cells, thus improving the audibility of adjacent cells and reducing scheduling complexity such as UE position measurement intervals.

In some embodiments, it is also possible to improve the accuracy of the U-TDOA method and UL-TDOA (described in "Background Techniques") from Chen et al. Up to 50 times. Applying some embodiments to Chen's UL-TDOA variants reduces the positioning error to 3 meters or less in 95% of all possible cases. In addition, this accuracy gain further reduces scheduling complexity and UE position measurement intervals.

Again, using this embodiment, the accuracy of Chen's UL-TDOA method can be improved by up to 50 times. Therefore, applying this embodiment to a variant of Chen's UL-TDOA would reduce the positioning error to 3 meters or less in 95% of all possible cases. In addition, this accuracy gain will further reduce scheduling complexity and UE position measurement intervals.

The DL-TDOA method and the U-TDOA / UL-TDOA method described above depend on one-way measurement (distance measurement). The present embodiment and virtually all other ranging techniques require that the PRS and / or other signals used in the unidirectional ranging process be frequency and phase coherent. OFDM-based systems such as LTE are frequency coherent. However, the UE unit and eNB are not phase-synchronized or time-synchronized up to several nanoseconds by a common source such as UTC, and for example, a random phase adder exists.

In order to avoid the influence of phase coherency on the distance measurement accuracy, the embodiment of the multipath processor calculates the distance measurement signal, for example, the reference signal and the differential phase between the individual components (subcarriers). This eliminates the need for a random phase term adder.

As mentioned above in the discussion of Chen et al., The application of the embodiments described herein results in a significant improvement in accuracy in the indoor environment compared to the performance achieved by Chen et al. For example, according to Chen et al., DL-OTDOA and / or U-TDOA / UL-TDOA are primarily for outdoor environments, and in indoor environments (buildings, campuses, etc.), DL-OTDOA and U-TDOA technologies are good. May not work. Several reasons are mentioned, such as the Distributed Antenna System (DAS), which is commonly used indoors (hence, each antenna does not have a unique ID) (see Chen, # 161-164). ]

The embodiments described below operate in wireless networks using OFDM modulation and / or derivatives thereof, as well as reference / pilot / and / or synchronization signals. Thus, the embodiments described below also operate in LTE networks and also in other radio systems and other radio networks, including other types of modulation, with or without reference / pilot / and / or synchronization signals. Applicable.

The approaches described herein are also applicable to other wireless networks such as WiMax, WiFi, and white spaces. For other radio networks that do not use reference / pilot signals and / or sync signals, one of the following other types of modulation embodiments, as described in US Pat. No. 7,872,583. One or more may be used: 1) part of the frame is dedicated to the distance measurement signal / distance measurement signal element; 2) the distance measurement signal element is embedded in the transmission / reception signal frame; and 3) measurement. Distance signal elements are embedded in the data.

An embodiment of the multipath mitigation range estimation algorithm described herein (also described in US Pat. Nos. 7,969,311 and 8,305,215) is a direct path of signal (DLOS). It works by providing range estimation with a set consisting of and multipath reflections.

The LTE DAS system produces multiple copies of the same signal seen at various time offsets for the mobile receiver (UE). The delay is used to uniquely determine the geometric relationship between the antenna and the mobile receiver. The signal recognized by the receiver is similar to the signal recognized in a multipath environment, except that the main "multipath" component results from the sum of the offset signals from multiple DAS antennas.

The set of signals recognized by the receiver is the same as the type of set of signals designed to be utilized by the embodiment, except that the main multipath component is not conventional multipath. .. The multipath mitigation processor (algorithm) can determine the DLOS and each path, such as reflection attenuation and propagation delay (see Equations 1-3 and related description). Although multipath may exist due to the distributed RF channel (environment), the main multipath component in this signal set is associated with transmission from multiple antennas. Embodiments of the present multipath algorithm can estimate these multipath components, separate the range of the DAS antenna to the receiver, and provide range data to the position processor (implemented in software). Depending on the shape of the antenna arrangement, this solution can provide both X, Y position coordinates and X, Y, Z position coordinates.

As a result, this embodiment does not require the addition of hardware and / or new network signals. In addition, positioning accuracy is achieved by 1) reducing multipath and 2) in the case of active DAS, by dramatically lowering the lower limit of ranging error (eg from about 50 meters to about 3 meters). Can be significantly improved.

It is assumed that the location (position) of each antenna of the DAS is known. The signal propagation delay for each antenna (or relative to other antennas) also needs to be determined (known).

In an active DAS system, the signal propagation delay can be determined automatically using a loopback technique, which causes a known signal to be transmitted back and forth and this round trip time to be measured. This loopback technique also eliminates changes (drifts) in signal propagation delay due to temperature, time, etc.

Using multiple macrocells and associated antennas, picocells and microcells further improve resolution by providing additional reference points.
The above embodiment of estimating individual ranges in a plurality of copies of a signal set from a plurality of antennas can be further improved by modifying the signal transmission structure in the following two ways. The first is time division multiplexing of transmission from each antenna. The second approach is frequency division multiplexing for each antenna. Both improvement methods, namely time division multiplexing and frequency division multiplexing, are used simultaneously to further improve the distance measurement and position accuracy of the system. Another approach is to add a propagation delay to each antenna. The delay value is large enough to exceed the delay spread within a particular DAS environment (channel), but smaller than the cyclic prefix (CP) so that the multipath caused by the additional delay does not result in ISI (intersymbol interference). Is selected.

Adding a unique ID, or unique identifier, for each antenna improves the efficiency of the resulting solution. For example, the processor eliminates the need to estimate the entire range from the signal from each antenna.

In one embodiment using LTE downlink, the subcarriers of one or more reference signals, such as the subcarriers of the pilot signal and / or the sync signal, are used to determine the phase and amplitude of the subcarriers, which are then then determined. Applied to multipath processors to reduce multipath interference and generate range-based position observers, it edits wildpoints using multilateration and position matching algorithms to identify position estimates.

Another embodiment takes advantage of the fact that LTE uplink signaling also includes a reference signal from the mobile device to the base station, which also includes a reference subcarrier. In fact, these sub-modes are used by the network to allocate frequency bands to uplink devices, from full-sound mode to modes in which a reference subcarrier is used to generate a channel impulse response and assist in decoding the uplink signal. There is one or more modes, including carrier waves. Also, similar to the DL PRS added in Release 9, additional UL reference signals may be added in this and future standard releases. In this embodiment, the uplink signal is processed by multiple base units (eNBs) and the same range is used for phase, multipath mitigation processing to generate the range associated with the observer. In this embodiment, the position matching algorithm is used as established by the multilateration algorithm to edit the wildpoint observations and generate position estimates.

In another embodiment, one or more reference (such as pilot and / or synchronous) subcarriers for both LTE downlink and LTE uplink are collected, a range to phase mapping is applied, and multipath mitigation is applied. , The range related to the observable is estimated. These data are then fused using a multilateration algorithm and a position matching algorithm to provide a more robust set of observables with respect to position. This advantage is the redundancy that provides increased accuracy because the downlink and uplink connect two different frequency bands, or, in the case of Time Division Duplex (TDD), the accuracy that improves system coherency. Let's go.

In a DAS (Distributed Antenna System) environment where multiple antennas transmit the same downlink signal from a microcell, the position matching algorithm is extended to reduce multipath from the subcarrier of the reference signal (such as pilot and / or synchronization). The range of the DAS antenna is separated from the observer generated by the process, and the position estimation is obtained from a plurality of DAS emitter (antenna) ranges.

In the DAS system (environment), accurate position estimates are only possible if the signal paths from the individual antennas can be resolved with high accuracy, so that the path error is the distance between the antennas. Only a small part of it (accuracy of 10 meters or less). All existing techniques / methods cannot provide such accuracy in a severe multipath environment (signals from multiple DAS antennas appear as induced severe multipath). / The method cannot utilize the extension of the above-mentioned position matching algorithm and this position identification method / technique in the DAS environment.

The InvisiTrack multipath mitigation methods and systems for object identification and position detection described in US Pat. No. 7,872,583 include range to signal phase mapping, multipath interference mitigation, and LTE downlink. , Uplink, and / or both (downlink and uplink), applied to the process of generating range-based position observers using subcarriers of one or more reference signals, using multilateration and position consistency. To generate position estimation.

In all of the above embodiments, a three-sided surveying positioning algorithm may also be used.

DL-OTDOA localization is defined in LTE Release 9 (“Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation; 3GPP TS 36.211 Release 9 technology”). However, the wireless operator (carrier) has not yet implemented it. On the other hand, downlink location can be performed within the current, eg, unmodified LTE network environment, by using existing physical layer measurement operations.

In LTE, UEs and eNBs are required to make physical layer measurements of radio characteristics. The measurement definition is defined in 3GPP TS 36.214. These measurements are performed on a regular basis and reported to the upper layers to support intra-frequency and inter-frequency handover, inter-radio access technology (inter-RAT) handover, timing measurements, and radio resource management (RRM). It is used for various purposes such as other purposes.

For example, RSRP (reference signal received power) is the average of the power of all resource elements carrying a cell-specific reference signal over the bandwidth.

Another embodiment is an RSRQ (reference signal reception quality) measurement that provides additional information (RSRQ combines signal strength and interference level).

The LTE network provides the UE with a list of eNB neighbors (for serving eNBs). Based on the network knowledge configuration, the (serving) eNodeB provides the UE with identifiers of neighboring eNBs and the like. The UE then measures the signal quality of the receivable neighborhood. The UE reports the result back to eNodeB. Note: The UE also measures the signal quality of the serving eNB.

According to the present specification, RSRP is defined as a linear average (in [W] units) of the power contribution of a resource element carrying a cell-specific reference signal within the measured frequency bandwidth considered. The measurement bandwidth used by the UE to determine the RSRP is left to the implementation of the UE, with the constraint that the corresponding measurement accuracy requirements must be met.

Given the measurement bandwidth accuracy requirements, this bandwidth is quite large and the cell-specific reference signals used in RSRP measurements can be further processed to determine the phase and amplitude of these reference signal subcarriers, and then Applied to multipath processors to reduce multipath interference and generate range-based position observers. In addition, other reference signals used for RSRP measurements, such as the secondary sync signal (SSS), may also be used.

Positional measurements can then be estimated using multilateration and alignment algorithms based on range observations from three or more cells.

As mentioned above, there are multiple causes of instability in RF fingerprinting databases, but one of the main causes is multipath (RF signatures are very sensitive to multipath). As a result, the positioning accuracy of RF fingerprinting methods / techniques is highly influenced by multipath dynamics and varies with time, environment (eg weather), movement of people and / or objects, Z height and / or device Z height and / or. Includes vertical uncertainty showing> 100% variability depending on the orientation of the antenna (see Tsung-Han Lin, et al. Microscopic Expanation of an RSSI-Signature-Based Indicator Position System).

In this embodiment, the ability to detect and characterize each individual path, including a significantly attenuated DLOS (multipath processor), can significantly improve the positioning accuracy of RF fingerprinting. As a result, RF fingerprinting decisions regarding position measurements can be supplemented with real-time multipath distribution information.

As mentioned above, position measurement requires temporal position reference synchronization. In wireless networks, these location criteria may include access points, macro / mini / pico, and femtocells, as well as so-called small cells (eNBs). However, wireless operators do not implement the synchronization accuracy required for accurate position measurements. For example, in the case of LTE, the standard does not require time synchronization between eNBs for FDD (Frequency Division Duplex) networks. In the case of LTE TDD (Time Division Duplex), the limit of this time synchronization accuracy is +/- 1.5 microseconds. This is equal to 400+ meters of localization uncertainty. The LTE FDD network is also synchronized, though not required. However, use a larger limit (> 1.5 microseconds).

The wireless LTE operator uses GPS / GNSS signals to synchronize the eNB with respect to frequency and time. Note: LTE eNBs must maintain a very accurate carrier frequency. Macro / minicells are 0.05 ppm and other types of cells are slightly less accurate (0.1 to 0.25 ppm). GPS / GNSS signals can also allow for better time synchronization accuracy than 10 nanoseconds required (for locating). However, network operators and network equipment manufacturers choose packet forwarding, such as Internet / Ethernet networking time synchronization, by using NTP (Network Time Protocol) and / or PTP (Precision Time Protocol), eg, IEEE 1588v2 PTP. Thus, we are trying to reduce the costs associated with GPS / GNSS units.

IP network-based synchronization has the potential to meet minimum frequency and time requirements, but lacks the GPS / GNSS accuracy required for position measurements.

The approach described herein is based on GPS / GNSS signals and signals generated by eNBs and / or APs or other wireless network equipment. It may also be based on IP networking synchronization signals and protocols, as well as signals generated by eNBs and / or APs or other wireless network equipment. This approach is also applicable to other wireless networks such as WiMax, WiFi, and white spaces.

The eNB signal is received by a time observation unit (TMO) installed in the operator's eNB facility (Fig. 12). The TMO also includes an external synchronous source input.

The eNB signal is processed by the TMO and time stamped using a clock that is synchronized with the external sync source input.

External synchronization sources can be from GPS / GNSS and / or Internet / Ethernet networking, such as PTP or NTP.

Time-stamped signals, such as LTE frame initiation (which can be other signals, especially in other networks), also include the location and / or cell ID of the eNB (cell) and via the Internet / Ethernet backhaul. It is sent to a central TMO server that creates, maintains, and updates a database of all eNBs.

UEs and / or eNBs involved in the distance measurement and position measurement acquisition process require the TMO server, which returns a time synchronization offset between the involved eNBs. These time-synchronized offsets will be used by UEs and / or eNBs involved in the process of acquiring and coordinating position measurements.

Alternatively, if the UE and / or eNB involved in the ranging process also supplies the acquired ranging information to the TMO server, the TMO server may perform position measurement calculations and adjustments. The TMO server then returns an accurate (adjusted) position (location) determination.

When two or more cell eNB devices are juxtaposed, a single TMO can process and time stamp signals from all eNBs.

RTT (Round Time Trip) measurements (distance measurement) may be used for positioning. The drawback is that RTT ranging is under the influence of multipath, which has a dramatic effect on positioning accuracy.

On the other hand, RTT position identification generally does not require position-based synchronization (time), and in the case of LTE, eNB is not particularly required.

At the same time, when operating with pilot reference and / or other signals of the wireless network, the multipath mitigation processors, methods / techniques, and algorithms described in US Pat. No. 7,872,583 are for RTT signals. The channel response can be determined, for example, the multipath channel through which the RTT signal is passing can be identified. This makes it possible to correct the RTT measurement and determine the actual DLOS time.

If the DLOS time is known, it would be possible to obtain position measurements using trilateration and / or similar positioning methods without the need for eNB or temporal position reference synchronization.

Even with the TMO and TMO server in place, the InvisiTrack technology integration requires changes in macro / mini / pico and small cells and / or UEs (cell phones). These changes are limited to SW / FW (software / firmware), but require a lot of effort to improve the existing infrastructure. In some cases, network operators and / or UE / mobile phone manufacturers / suppliers resist equipment changes. Note: The UE is a wireless network user device.

When the functions of TMO and TMO are extended to support InvisiTrack positioning technology, this SW / FW change can be completely avoided. In other words, another embodiment described below operates on a wireless network signal, but does not require modification of the wireless network equipment / infrastructure. Therefore, the embodiments described below operate on LTE networks and are also applicable to other wireless systems / networks such as Wi-Fi.

In essence, this embodiment forms a parallel radio locating infrastructure that uses radio network signals to obtain location measurements.

Similar to TMOs and TMO servers, InvisiTrack's locating infrastructure collects data from one or more wireless network signal acquisition units (NSAUs), as well as NSAUs, analyzes the data, and determines range and location. Will consist of one or more relocation server units (LSUs) that instantly translate it into, for example, a table of telephone / UE IDs and locations. The LSU interfaces with the wireless network via the network API.

Multiple of these units can be deployed at various locations within a large infrastructure. If the NSAU has coherent timing, all results can be used, which gives even better accuracy.

Coherent timing can be derived from GPS clocks and / or other stable clock sources.

NSAU communicates with LSU via local area network (LAN), metropolitan area network (MAN) and / or the Internet.

Equipment / In some cases, NSAU and LSU can be combined / integrated into a single unit.

In order to support location services using LTE or other wireless networks, transmitters need to be clock-synchronized and event-synchronized within tight tolerances. This is usually achieved by locking to the GPS 1PPS signal. This results in timing synchronization within 3 nanoseconds (1σ) in the local area.

However, there are many cases where this type of synchronization is not practical. The present embodiment provides a time offset estimate between the downlink transmitter and the tracking of the time offset in order to provide a delay compensation value to the position process so that the position process allows the transmitter to clock synchronize and You can proceed as if the events were synchronized. This is achieved by prior knowledge of the transmitting antenna (required for all location services) and the receiver whose speculative antenna location is known. This receiver, called a synchronization unit, will collect data from all downlink transmitters and calculate the offset timing from a preselected base antenna, assuming the location is known. These offsets are tracked by the system using a tracking algorithm that compensates for downlink transmitter clock drift. Note: The process of deriving the pseudorange from the received data uses the InvisiTrack multipath mitigation algorithm (described in US Pat. No. 7,872,583). Therefore, synchronization is not affected by multipath.

These offset data are used by the location processor (location server, LSU) to properly align the data from each downlink transmitter and, as a result, appear to be generated by the synchronous transmitter. The time accuracy is comparable to the best 1PPS tracking and supports a position accuracy of 3 meters (1σ).

Synchronous receivers and / or receiver antennas are positioned based on optimal GDOP for best performance. In large equipment, multiple synchronization receivers can be used to provide equivalent 3 nanometer seconds (1σ) synchronization offsets across the network. The use of synchronous receivers eliminates the requirement for downlink transmitter synchronization.

The synchronous receiving unit can be a stand-alone unit that communicates with NSAU and / or LSU. Alternatively, this synchronous receiver can be integrated with NSAU.

A diagram of an exemplary wireless network locating device is shown in FIG.

An embodiment of a fully autonomous system that uses LTE signals (no network investment on the customer side is required) uses LTE signals that operate in the following modes.
1. 1. Uplink mode: Uses wireless network uplink (UL) signals for location (FIGS. 16 and 17).
2. Downlink mode: Uses a wireless network downlink (DL) signal for location (FIGS. 14 and 15).
3. 3. Bidirectional mode: Both UL signal and DL signal are used for positioning.
In uplink mode, multiple antennas are connected to one or more NSAUs. These antenna positions are independent of the radio network antenna, and the NSAU antenna position is chosen to minimize precision geometric dilution (GDOP).

The network RF signals from the UE / mobile phone device are collected by the NSAU antenna and processed by the NSAU during a time interval sufficient to capture one or more instances of all signals of interest. Generate a time-stamped sample of the RF signal of the processed network.

Optionally, the NSAU also receives, processes, and time stamps a sample of the downlink signal to obtain additional information, such as for determining the UE / telephone ID.

From the captured, time-stamped sample, the UE / mobile phone device identification number (ID) is determined (acquired) along with the time-stamped wireless network signal of interest associated with each UE / mobile phone ID. Will be). This operation can be performed by either NSAU or LSU.

NSAU periodically supplies data to LSU. If the IDs of one or more UEs / mobile phones require unscheduled data, LSU requests additional data.

In UL mode operation, no changes / modifications are required in the wireless network infrastructure and / or existing UE / mobile phones.

Downlink (DL) mode requires a UE with InvisiTrack enabled. Also, if the phone is used to obtain a position measurement, the mobile phone FW needs to be modified.

In some cases, the operator can enable the baseband signal from the BBU (baseband unit). In such cases, the NSAU can also process these available baseband radio network signals instead of the RF radio network signals.

In DL mode, it is not necessary to associate the UE / mobile phone ID with one or more wireless network signals. It processes these signals on the UE / mobile phone, or the UE / mobile phone periodically generates time-stamped samples of the RF signals of the processed network and sends them to the LSU. This is because the LSU sends the result back to the UE / mobile phone.

In DL mode, NSAU processes RF or baseband (if available) radio network signals and time stamps the processed signals. From the captured and time stamped sample, the DL frame start of the radio network signal associated with the network antenna is determined (acquired) and the difference (offset) between these frame starts is calculated. This operation can be performed by either NSAU or LSU. The frame start offset of the network antenna is stored in the LSU.

In DL mode, when the device uses InvisiTrack technology to process / determine its position measurement, the frame offset start of the network antenna is transmitted from the LSU to the UE / telephone device. Otherwise, when the UE / mobile phone device periodically sends a time-stamped sample of the processed network's RF signal to the LSU, the LSU determines the device's position measurement and sends the position measurement data. Call back to the device.

In DL mode, the radio network RF signal is transmitted from one or more radio network antennas. To avoid the multipath effect on the accuracy of the result, the RF signal should be intercepted from the antenna or the antenna connection to the wireless network equipment.

The bidirectional mode includes determining the position measurement from both UL and DL operations. Thereby, the position identification accuracy can be further improved.

Some enterprise setups use one or more BBUs that supply one or more remote radio heads (RRHs), and then each RRH supplies the same ID to multiple antennas. In such an environment, it may not be essential to determine the DL mode frame start offset of the network antenna, depending on the wireless network configuration. This includes a single BBU setup, as well as multiple BBUs, where the antenna for each BBU is assigned to a particular zone and the coverage of adjacent zones overlaps.

On the other hand, in a configuration in which antennas supplied from a plurality of BBUs are alternately arranged in the same zone, it is necessary to determine the frame start of the DL mode of the network antenna.

In the DL operation mode in the DAS environment, a plurality of antennas may share the same ID.

In this embodiment, the position matching algorithm has been extended / developed to separate the range of the DAS antenna from the observables generated by the multipath mitigation process from the subcarriers of the reference signal (such as pilot and / or synchronization). , Obtain position estimates from multiple DAS emitter (antenna) ranges.

However, these matching algorithms are limited by the number of antennas that issue the same ID. The number of antennas that issue the same ID can be reduced by the following method.
1. 1. In a given coverage zone, antennas supplied from different sectors of the sectorized BBU are alternated (the BBU can support up to 6 sectors).
2. In a given coverage zone, different sectors of the sectorized BBU and antennas supplied by different BBUs are alternated.
3. 3. Add a propagation delay element to each antenna. The delay value is large enough to exceed the delay spread within a particular DAS environment (channel), but smaller than the cyclic prefix (CP) so that the multipath caused by the additional delay does not result in ISI (intersymbol interference). Is selected. By adding a delay ID unique to one or more antennas, the number of antennas that issue the same ID is further reduced.

In one embodiment, an autonomous system can be provided that does not involve network investment on the customer side. In such an embodiment, the system can operate in a frequency band other than the LTE band. For example, ISM (industrial science and medical) bands and / or white space bands may be used where LTE services are not available.

The present embodiment may also be integrated into macro / mini / pico / femto stations and / or UE (mobile phone) devices. The integration may require network investment on the customer side, but overhead costs can be reduced and TCO (total cost of ownership) can be dramatically improved.

As mentioned above, the PRS can be used by UEs for downlink observation arrival time difference (DL-OTDOA) positioning. Regarding the synchronization of adjacent base stations (eNBs), 3GPP TS 36.305 (Stage 2 functional specification of User Positioning in E-UTRAN) defines the transfer timing to the UE. This is the timing for the eNodeB service of the candidate cell (for example, the adjacent cell). 3GPP TS 36.305 also defines the physical cell ID (PCI) and global cell ID (GCI) of candidate cells for measurement.

According to 3GPP TS 36.305, this information is distributed from the E-MLC (Enhanced Serving Mobile Location Center) server. Note that TS 36.305 does not specify the timing accuracy described above.

3GPP TS 36.305 also specifies that the UE returns downlink measurements, including reference signal time difference (RSTD) measurements, to the E-MLC.

RSTD is a measurement obtained between a pair of eNBs (see TS 36.214 Evolved Universal Radio Access (E-UTRA); Physical Layer measurement; Release 9). This measured value is defined as the relative timing difference between the subframe received from the adjacent cell j and the corresponding subframe of the serving cell i. Positioning reference signals are used to obtain these measurements. The result is reported back to the location server that calculates the location.

In one embodiment, the hybrid method can be defined to accommodate both the newly introduced PRS and the existing reference signal. In other words, the hybrid method can be used / operated with PRS, with other reference signals (eg, cell or node specific reference signal (CRS)), or with both signal types.

Such a hybrid method has the advantage of allowing the network operator to dynamically select the operating mode according to the situation or network parameters. For example, PRS has better listening ability than CRS, but can reduce data throughput by up to 7%. On the other hand, the CRS signal does not cause a reduction in throughput. In addition, the CRS signal is backwards compatible with all previous LTE releases, such as Release 8 and below. Therefore, the hybrid method provides network operators with the ability to make trade-offs, or balance, between listening ability, throughput, and compatibility.

In the Long Term Evolution (LTE) embodiment, the LTE downlink baseband signal (generated by the cell or radio node, referred to herein as a "node") is generally incorporated into the downlink frame. The receiver for detecting and receiving such a signal may detect downlink frames from a plurality of cells or nodes (two or more). Each downlink frame contains a plurality of CRS or reference signals. In downlink (DL) frames, these reference signals have predetermined positions in time and frequency, for example, there is a deterministic time offset between the start of the frame and each CRS within a given frame. do.

In addition, each CRS is modulated with a special code. Modulation and code are also predetermined. The CRS modulation is the same for all nodes, but the code (seed) is determined by the node's ID (identification) number.

As a result, by knowing the node ID, it is possible to estimate the course position of the frame start time of each frame from each node (cell) in the spectrum of the reference signal. To do so, it is first necessary to determine the frame start time or frame start for all DL signals from different nodes. For example, in one embodiment, the received DL baseband signal is correlated with a code-modulated CRS (generated internally by a detector and / or a multipath mitigation processor) from all nodes. A CRS sequence or other reference signal can be found and this information is used to find the coarse frame start of all observable nodes. In one embodiment, the detector can also demodulate / decode the CRS and then correlate the demodulated / decoded CRS with the baseband subcarrier assigned to the CRS.

At the same time, in one embodiment, the CRS may also be used as a ranging signal by a multipath mitigation processor. Therefore, in addition to finding the coarse frame start, the detector correlation process separates the CRS from other signals in the frame (such as the payload) using the code used to modulate these signals. You can also. These separated CRSs and associated frame starts are then transferred to the multipath mitigation processor for distance measurement.

A similar approach can be used in uplink mode to determine the timing offset between different node receivers.

In a downlink embodiment, the system for tracking and locating one or more wireless network devices communicating with the network receives multiple signals from two or more nodes communicating with the network. In the configured user equipment receiver, the plurality of signals are modulated by a code determined by identifying each node of two or more nodes transmitting the plurality of signals, and the user equipment receiver is this. Each to track and locate user equipment receivers and one or more wireless network devices, including detectors configured to detect and separate reference signals from multiple signals based on identification. It comprises a processor configured to use a reference signal as a distance measurement signal from a node.

In this embodiment, a plurality of signals from each node of two or more nodes are incorporated into a frame containing a reference signal, and the detector is further configured to estimate the course position of the frame from each node. ..

In this embodiment, the detector is further configured to estimate the course position by correlating the reference signal with a known replica of the reference signal.

In this embodiment, the detector is further configured to separate the reference signal from any other signal in the frame, and the detector is to separate the reference signal of each node of two or more nodes. It is further configured in.

In the present embodiment, the processor is at least one multipath mitigation processor, such that the multipath mitigation processor receives the course position and the separated reference signal and estimates the relative arrival time of the ranging signal from each node. It is configured in.

In this embodiment, the processor is at least one multipath mitigation processor.

In this embodiment, a plurality of signals from each node of two or more nodes are in the frame, and the detector is further configured to estimate the course position of the frame start from each node, and the detector. Is configured to separate the reference signal from any other signal in the frame, and the detector is further configured to separate the reference signal of each node of two or more nodes. The instrument is configured to pass the course position of each node and the separated reference signal to the multipath mitigation processor, which receives the course position and the separated reference signal from each node. It is configured to estimate the relative arrival time of the ranging signal.

In this embodiment, the system includes an uplink embodiment in which the node receiver is configured to receive device signals from one or more wireless network devices, where the device signals transmit the device signals. Modulated with a device code determined by identifying the device of each wireless network device of one or more wireless network devices, the node receiver detects the device reference signal based on the device identification and separates it from the device signal. A second processor, including a device detector configured as described above, uses a device reference signal as a ranging signal from each wireless network device to track and locate one or more wireless network devices. It is configured as follows.

In one embodiment, the system for tracking and locating one or more wireless network devices communicating with the network is configured to receive multiple signals from two or more nodes communicating with the network. A device receiver, the plurality of signals being modulated by a code determined by identifying each node of two or more nodes transmitting the plurality of signals, based on the user device receiver and this identification. To detect and separate reference signals from multiple signals, and to use reference signals as distance measurement signals from each node to track and locate one or more wireless network devices. It includes a configured processor.

In this embodiment, a plurality of signals from each node of two or more nodes are incorporated into a frame containing a reference signal, and the processor is further configured to estimate the course position of the frame from each node.

In this embodiment, the processor is further configured to estimate the course position by correlating the reference signal with a known replica of the reference signal.

In this embodiment, the processor is further configured to estimate the relative arrival time of the ranging signal from each node based on the course position and the separated reference signal.

In this embodiment, the processor is further configured to separate the reference signal from any other signal in the frame, and the processor is further configured to separate the reference signal of each node of the two or more nodes. It is configured.

In this embodiment, a plurality of signals from each node of two or more nodes are in the frame, and the processor estimates the course position of the frame start by correlating the reference signal with a known replica of the reference signal. The processor is further configured to separate the reference signal from any other signal in the frame, and to separate the reference signal of each node of two or more nodes. The processor is further configured to estimate the relative arrival time of the ranging signal from each node based on the course position and the separated reference signal.

In one embodiment, the system for tracking and locating one or more wireless network devices communicating with the network is to receive multiple signals from two or more nodes communicating with the network. The signal is modulated by a code determined by identifying each node of two or more nodes transmitting the plurality of signals, and based on this identification, a reference signal is detected from the plurality of signals. A detector that separates and performs, and a processor that is configured to use a reference signal as a ranging signal from each node to track and locate one or more wireless network devices. Be prepared.

In this embodiment, a plurality of signals from each node of two or more nodes are incorporated into a frame containing a reference signal, and the detector is further configured to estimate the course position of the frame from each node. ..

In this embodiment, the detector is further configured to estimate the course position by correlating the reference signal with a known replica of the reference signal.

In this embodiment, the detector is further configured to separate the reference signal from any other signal in the frame, and the detector is to separate the reference signal of each node of two or more nodes. It is further configured in.

In the present embodiment, the processor is at least one multipath mitigation processor, such that the multipath mitigation processor receives the course position and the separated reference signal and estimates the relative arrival time of the ranging signal from each node. It is configured in.

In this embodiment, the processor is at least one multipath mitigation processor.

In this embodiment, a plurality of signals from each node of two or more nodes are in the frame, and the detector is further configured to estimate the course position of the frame start from each node, and the detector. Is configured to separate the reference signal from any other signal in the frame, and the detector is further configured to separate the reference signal of each node of two or more nodes. The instrument is configured to pass the course position of each node and the separated reference signal to the multipath mitigation processor, which receives the course position and the separated reference signal from each node. It is configured to estimate the relative arrival time of the ranging signal.

In one embodiment, the system for tracking and locating one or more wireless network devices communicating with the network is a node receiver configured to receive device signals from the one or more wireless network devices. The device signal is modulated with a device code determined by identifying the device of each wireless network device of one or more wireless network devices transmitting the device signal, and the node receiver is responsible for identifying the device. Each wireless network to track and locate node receivers and one or more wireless network devices, including device detectors that are configured to detect and isolate the device reference signal based on the device signal. It comprises a processor configured to use a device reference signal as a ranging signal from the device.

In addition, the hybrid method can be transparent to the LTE UE positioning architecture. For example, the hybrid method can operate with the 3GPP TS 36.305 framework.

In one embodiment, the RSTD can be measured and transferred from the UE to the E-SMLC according to 3GPP TS 36.305.

UUL TDOA (U-TDOA) is currently in the research stage and is expected to be standardized in future release 11.

Embodiments of UL-TDOA (uplink) are described herein above and are also shown in FIGS. 16 and 17. 18 and 19 below provide examples of another embodiment of UL-TDOA.

FIG. 18 shows an environment that may include one or more DAS and / or femto / small cell antennas. In this exemplary embodiment, each NSAU comprises a single antenna. As shown, at least three NSAUs are required. However, since each UE needs to be "listened" to at least three NSAUs, additional NSAUs may be added to improve audibility.

In addition, the NSAU may be configured as a receiver. For example, each NSAU receives information wirelessly but does not transmit it. During operation, each NSAU can hear the wireless uplink network signal from the UE. Each of the UEs can be a mobile phone, a tag, and / or another UE device.

Further, the NSAU may be configured to communicate with a location-specific server unit (LSU) via an interface such as a wired service or LAN. The LSU can then communicate with the radio or LTE network. The communication can be via the network API, the LSU can communicate with, for example, the LTE network E-SMLC, and wired services such as LAN and / or WAN can be used.

Optionally, the LSU may also communicate directly with the DAS base station and / or the femto / small cell. This communication can use the same or modified network API.

In this embodiment, a sounding reference signal (SRS) can be used for locating. However, other signals may also be used.

The NSAU can convert the UE uplink transmission signal into a digital format, such as an I / Q sample, and can periodically transmit a large number of converted signals to the LSU along with a time stamp.

The DAS base station and / or femto / small cell can pass one or all of the following data to the LSU.
1) SRS, I / Q samples, and time stamps;
2) A list of served UE IDs; and 3) SRS schedules (including SRS SchedulingRequestConfig information and SRS-UL-Config information) and UE IDs for each UE.

The information passed to LSU may not be limited to the above information. It may contain any information needed to correlate the uplink signal of a UE device, such as a UE SRS, with its respective UE ID.

The LSU function may include distance measurement calculation, acquisition of UE position measurement. These decisions / calculations may be based on information passed to the LSU from NSAUs, DAS base stations, and / or femto / small cells.

The LSU may also determine the timing offset from the available downlink transmission information passed from the NSAU to the LSU.

The LSU can then provide UE location measurements and other calculations and data to the wireless or LTE network. Such information may be communicated via the network API.

For synchronization, each NSAU may receive, process, and time stamp a sample of the downlink signal. Each NSAU may also periodically send a large number of such samples to the LSU, including time stamps.

In addition, each NSAU may include an input configured for synchronization with an external signal.

FIG. 19 shows another embodiment of UL-TDOA. The environment of this embodiment may include, in addition to the components shown in FIG. 18, one or more cell towers that can be used in place of DAS base stations and / or femto / small cells. Data from one or more cell towers can be used to obtain UE position measurements.

Therefore, an advantage of this embodiment is that the position measurement is acquired using only a single cell tower (eNB). In addition, this embodiment is configured to behave similarly to the method described in FIG. 18, except that one or more eNBs can replace DAS base stations and / or femto / small cells. obtain.

One method of determining the uplink position of the UE is the cell identification method (CID). In the basic CID method, the UE position can be determined at the cell level. This method is purely network-based. As a result, the UE, such as the handset, is unaware of the fact that it is being tracked. This is a relatively simple method, but it lacks accuracy because the positioning uncertainty is equal to the cell diameter. For example, as shown in FIG. 20, not all handset 2000s in the cell diameter 2002 of the serving cell tower 2004 are in the same position, but have substantially the same position. The accuracy of the CID method can be improved when combined with knowledge of serving sector IDs (sector IDs). For example, as shown in FIG. 21, sector ID 2100 in cell diameter 2002 includes a number of handset 2104s known to have different positions than other handset 2000 in other sectors of cell diameter 2002. Identify section 2102.

Further improvements to the CID method may be possible by the extended cell ID (E-CID) method, which provides further improvements to the basic CID method described above. One remedy is to use timing measurements to calculate how far the UE is from the eNB (network node). This distance can be calculated as half the round trip time (RTT), that is, the timing advance (TA) (LTE TA) in LTE multiplied by the speed of light. If the UE is connected, RTT or TA may be used for distance estimation. In this case, both the serving cell tower or sector and the UE (at the time of the serving eNB command) measure the timing difference between the Rx subframe and the Tx subframe. The UE reports its measurements to the eNB (also under the control of the eNB). Note that LTE Release 9 adds TA type 2 measurements that depend on the timing advance estimated from the reception of the PRACH preamble during the random access procedure. The physical / packet random access channel (PRACH) preamble specifies the maximum number of preambles transmitted during a PRACH ramping cycle if no response is received from the tracked UE. The LTE type 1 TA measurement is equal to the RTT measurement as follows.
RTT = TA (type 1) = eNB (Rx-Tx) + UE (Rx-Tx)
With knowledge of the coordinates of the eNB and the height of the serving cell tower antenna, the position of the UE can be calculated by the network.

However, the E-CID positioning method is still limited. This is because in one dimension the position accuracy depends on the sector width and the distance from the serving cell tower, and in another dimension the error depends on the TA (RTT) measurement accuracy. The sector width varies depending on the network topology and is affected by the propagation phenomenon, specifically multipath. Estimates of sector accuracy vary from 200 meters to over 500 meters. The measurement resolution of LTE TA is 4 Ts, which corresponds to a maximum error of 39 meters. However, the actual error in LTE TA measurements is even greater due to calibration inaccuracies and propagation phenomena (multipath), which can reach up to 200 meters.

As shown in FIG. 22, the E-CID method can be further improved by adding a feature known as the angle of arrival (AoA). The eNB uses a linear array of equally spaced antenna elements 2200 to estimate the transmit direction of the UE. Typically, a reference signal is used for AoA measurement. When a reference signal from the UE is received by the two adjacent antenna elements 2200, the reference signal may be phase-rotated by an amount depending on the AoA, carrier frequency, and element spacing, as shown in FIG. In AoA, each eNB needs to have an antenna array / adaptive antenna. It is also exposed to multipath and topology variations. Nonetheless, advanced antenna arrays can significantly reduce the width 2202 of sectors 2100, which can result in better positioning accuracy. Further, when two or more serving cell towers 2300 (eNB base stations having a directional antenna array) are used to perform handset AoA measurement as shown in FIG. 23, the accuracy can be significantly improved. In such cases, accuracy is still under the influence of the multipath / propagation phenomenon.

Deploying antenna arrays / adaptive antennas across multiple LTE bands across the network requires tremendous effort in terms of capital, time, maintenance, and so on. As a result, the antenna array / adaptive antenna is not deployed for UE positioning. Other approaches, such as signal strength-based methods, do not result in significant accuracy improvements. One such signal strength approach is fingerprinting, which requires creating a fingerprinting database and continuously updating its large number of continuous changes (in time), for example, accuracy. It does not improve much, but it requires huge capital and recurring costs. In addition, fingerprinting is a UE-based technology that does not allow UE positioning without UE assistance at the UE's application level.

Another solution to the constraints of the uplink position method involves using the AoA function, which does not require an antenna array / adaptive antenna. Such an embodiment may use the TDOA (arrival time difference) positioning technique for AoA measurement and may be based on estimating the arrival time difference of signals from sources in a plurality of receivers. A particular value of time difference estimation defines a hyperbola between two receivers communicating with the UE. If the distance between the receiving antennas is small relative to the distance of the emitter (handset) in which it is located, the TDOA is equal to the angle between the baseline of the sensor (receiving antenna) and the incident RF energy from the emitter. If the angle between the baseline and true north is known, the quadrant azimuth line (LOB) and / or AoA can be determined.

Common locating methods using either TDOA or LOB (also known as AoA) are known, but the TDOA reference points are close to each other to allow the accuracy of such techniques. It is not used to measure LOB because it is too much. Rather, LOBs are typically measured using directional antennas and / or beam forming antennas. However, the superresolution methods described herein allow TDOA to be used for LOB measurements while dramatically improving accuracy. In addition, without using the reference signal processing techniques described herein, for example, by a non-serving sector and / or antenna, a reference signal arriving from a UE outside the serving sector is "listened", eg, detected. That can be impossible. Without the resolution and processing power described herein, it is not possible to use TDOA for LOB measurements because at least two reference points (eg, two or more sectors and / or antennas) are required. obtain. Similarly, the UE may not be able to detect a reference signal arriving at the UE from other than the serving sector, for example, from a non-serving sector and / or an antenna.

For example, FIG. 24 shows two scenarios, one is when the separation is wide and the other is when the separation is close (small). In both scenarios, the hyperbola 2400 and the incident line 2402 intersect at the position of the handset 2000, but if the separation of the antenna 2404 is wide, this will occur on a steep slope, greatly reducing the positioning error. At the same time, if the antennas 2404 are in close proximity to each other, the hyperbola 2400 becomes interchangeable with the RF energy incident, i.e. the LOB / AoA line 2402.

式中、
Δｔは、時間差（秒単位）であり、
ｘは、２つのセンサ間の距離（メートル単位）であり、
Θは、センサの基線と入射ＲＦ波との間の角度（度単位）であり、
ｃは光速である。 The formula described below can be used to determine the incident RF energy from the emitter, and the time difference in the arrival time of RF energy between the two antennas (sensors) can be obtained by the following formula.

During the ceremony
Δt is the time difference (in seconds),
x is the distance (in meters) between the two sensors
Θ is the angle (in degrees) between the baseline of the sensor and the incident RF wave.
c is the speed of light.

By using the TDOA locating embodiment, some locating strategies, i.e., (1) TDOA measurements (multilateration) between two or more serving cells, are available, for example, for example. Wide isolation, (2) TDOA measurements can only be obtained from two or more sectors in one or more serving cells, for example, small antenna isolation (LOB / AoA, etc.), (3) strategy (2). Combinations with (3) and (4) TA measurements with strategies (1)-(3) (eg, improved E-CID) can be used.

As will be further described below, in the case of closely arranged antennas, the TDOA positioning embodiment uses quadrant azimuth lines when the signals from two or more antennas are from the same cell tower. good. These signals can be detected by the received decoding signal. By knowing the position of the tower and the azimuth angle of each sector and / or antenna, the quadrant azimuth line and / or AoA can be calculated and used in the position identification process. The accuracy of LOB / AoA may be affected by multipath, noise (SNR), and the like. However, this effect may be mitigated by advanced signal processing and the multipath mitigation processing techniques described above (which can be based on super-resolution techniques). Such advanced signal processing includes, but is not limited to, signal correlation / correlation, filtering, averaging, synchronous averaging, and other methods / techniques.

The serving cell tower 2500 typically consists of a plurality of sectors, as shown in FIG. 25, which shows a three-sector (sector A, sector B, and sector C) configuration. The three-sector deployment shown may include one or more antennas 2502 per sector. A single sector, such as sector A, may be under the control of the UE (handset) because the handset transmission is within the main lobe of sector A (the center of the main lobe coincides with the sector azimuth). At the same time, the handset transmission will, for example, depart from the main lobes of sectors B and C and enter, for example, the side lobes of the antenna. Therefore, the handset signal is still present in the output signal spectrum of sector B and sector C, but will be significantly weakened against signals from other handsets located within the main lobe of sector B or sector C. Nevertheless, by using advanced signal processing, as described above and below, sufficient processing gain can be obtained from the ranging signal from the side lobes of adjacent sectors, such as the side lobes of sector B and sector C. Allows these signals to be detected. LTE uplink SRS (sounding reference signal) can be used as a ranging signal for network-based locating.

In other words, the UE uplink reference signal can be in the sidelobe of adjacent sector antennas, but the processing gain of the reference signal processing method described herein is between two (or more) sector antennas. It may be sufficient to enable the calculation of the TDOA of. The accuracy of this embodiment can be significantly improved by the multipath mitigation processing algorithm described above. Therefore, the LOB / AOA that intersects the ring calculated by LTE TA timing can provide the UE position within an error ellipse of approximately 20m x 100m.

Further reduction of positioning error may be achieved when the UE can be heard by two or more LTE towers, which is likely when using the processing gain and multipath mitigation techniques described above. In such cases, the intersection of the TDOA hyperbola with one or more LOB / AoA lines may result in an error ellipse of 30 x 20 meters (in the case of two sector cell towers). If each cell tower supports more than two sectors, the error ellipse can be further reduced to 10-15 meters. If the UE is listened to by three or more eNBs (cell towers), an accuracy of 5-10 meters can be achieved. In high-priced areas such as shopping malls and office parks, additional small cells or passive listening devices can be used to form the required coverage.

As mentioned above, each sector of the cell tower 2500 may include one or more antennas 2502. In a typical facility, for a given sector, the signals from each antenna are integrated at the sector's receiver input. As a result, for positioning purposes, two or more sector antennas can be considered as a single antenna with a composite directional pattern, azimuth, and elevation. The composite orientation of the virtual antenna and its (main lobe) azimuth and elevation may also be assigned to the sector itself.

In one embodiment, signals received (in digital form) from all sectors of each serving tower and adjacent serving cell towers are transmitted to a positioning server unit (LSU) for positioning. Further, the SRS schedule and TA measurement value for each UE to be served are provided to the LSU from each serving cell tower by each serving sector. Assuming that the position coordinates of each serving cell tower and each adjacent cell tower, the number of sectors per tower with the azimuth and elevation of each virtual (composite) sector antenna, and the position of each sector in the cell tower are known, the LSU is , The position of each UE with respect to the serving cell tower and / or the adjacent cell tower may be determined. All of the above information may be transmitted over a wired network, such as a LAN, WAN, etc., using one or more standard or dedicated interfaces. The LSU may also interface the wireless network infrastructure using standard interfaces and / or defined interfaces / APIs of network carriers. Positioning may also be split between the network node and the LSU, or may be performed solely on the network node.

In one embodiment, the positioning may be performed within the UE or may be split between the UE and the LSU or network node. In such cases, the UE may communicate wirelessly using standard networking protocols / interfaces. In addition, location determination can be performed by a combination of UE, LSU and / or network nodes, or the LSU function can be used in place of the LSU SUPL server, E-SMLC server, and / or LCS (location). Service) Can be incorporated (incorporated) into the system.

The embodiment of the downlink (DL) position identification method contradicts the above-mentioned embodiment of the uplink (UL) position identification method. In a DL embodiment, the sector can be a transmitter having a transmission pattern, azimuth, and elevation that match the receiving direction, azimuth, and elevation of the sector. Unlike the uplink embodiment, in the DL embodiment the UE typically has a single receiving antenna. Therefore, in the UE, there is no sensor baseline that can be used to measure the incident of RF waves. However, the UE can determine the TDOA between different sectors, and thus the hyperbola (multilateration) between the sectors, and since the sectors of the same cell tower are close to each other, the hyperbola can be seen in FIG. 24. As mentioned above, it can be exchanged for RF energy incident lines or LOB / AoA. The accuracy of LOB / AoA may be affected by multipath, noise (SNR), etc., but this effect is achieved by using advanced signal processing and multipath mitigation processing based on the above super-resolution technology. Can be mitigated.

As described above, UE DL locating can be achieved in a manner similar to UE uplink locating, except that the angle of incidence of the RF wave cannot be determined from the above equation. Instead, a multilateration technique may be used to determine the LOB / AoA of each serving cell tower.

The UE DL positioning embodiment also uses a reference signal. In the case of DL, one approach for such network-based locating may be to use an LTE cell-specific reference signal (CRS) as the ranging signal. Also, the position reference signal (PRS) introduced in LTE Release 9 may be used. Therefore, positioning may be performed using CRS only, PRS only, or both CRS and PRS.

Similar to the UE Uplink Positioning Embodiment, in the UE Downlink Positioning Embodiment, a snapshot of the UE received signal in digital form may be transmitted to the LSU for processing. The UE may also take TA measurements and provide them to the LSU. Optionally, TA measurements for each served UE may be provided to the LSU from each serving cell tower (network node) by each serving sector. As mentioned above, assuming that the position coordinates of each serving cell tower and each adjacent cell tower, the number of sectors per tower having the azimuth and elevation of the transmission pattern of each sector, and the position of each sector in the tower are known, the LSU Can determine the position of each UE with respect to the serving cell tower and / or the adjacent cell tower. In some embodiments, the positioning may be performed within the UE or may be split between the UE and the LSU or network node. In some embodiments, all positioning may be performed within the LSU or network node, or may be split between the two.

The UE will wirelessly communicate / receive measurement results and other information using standard wireless protocols / interfaces. Information exchange between the LSU and the network node may take place over a wired network, such as a LAN, WAN, etc., using a dedicated interface and / or one or more standard interfaces. The LSU may interface the wireless network infrastructure using standard interfaces and / or network carrier defined interfaces / APIs. Positioning may also be split between the network node and the LSU, or may be performed solely on the network node.

In the UE DL position embodiment described above, antenna port mapping information may be used to determine the position. The 3GPP TS 36.211 LTE standard defines antenna ports for DL. A separate reference signal (pilot signal) is defined in the LTE standard for each antenna port. Therefore, the DL signal also transmits antenna port information. This information is included in the PDSCH (Physical Downlink Shared Channel). The PDSCH uses antenna ports 0; 0 and 1; 0, 1, 2, and 3); or 5. These logical antenna ports are assigned (mapped) to physical transmitting antennas, as shown in FIG. As a result, this antenna port information can be used for the antenna identifier (antenna ID).

For example, antenna port mapping information can be used to determine RF wave incidence and hyperbola (multilateration) between antennas (assuming the antenna position is known). Depending on where the positioning is performed, the antenna mapping information must be available on the LSU or UE, or network node. Note that antenna ports are indicated by placing CRS signals in different time slots and different resource elements. Only one CRS signal is transmitted per DL antenna port.

When deploying MIMO (Multi-Input Multi-Output) on an eNB or network node, the receiver may be able to measure the arrival time difference from a given UE. Knowledge of antenna mapping to the receiver, including MIMO mapping, including antenna position, is used to determine the incidence of RF waves on the antenna (LOB / AoA) and the hyperbola (multilateration) for a given eNB antenna. It may also be possible. Similarly, in the UE, the UE receiver can determine the arrival time difference from two or more eNBs or network nodes and MIMO antennas. It is also possible to determine the incident of RF waves from the antenna (LOB / AoA) and the hyperbola (multilateration) of a given eNB antenna using knowledge of the position of the eNB antenna and the mapping of the antenna. Depending on where the positioning is performed, the antenna mapping information must be available on the LSU or UE, or network node.

There are other configurations that are a subset of MIMO, such as single-input multiple-output (SIMO), single-output multiple-output (SOMI), and single-input single-output (SISO). All of these configurations can be defined / determined by antenna port mapping and / or MIMO antenna mapping information for positioning.

In one aspect, the present embodiment relates to methods and systems for identifying, tracking, and locating RF-based objects, such as RTLS. According to one embodiment, the method and system use geographically dispersed clusters of receivers and / or transmitters that are precisely (eg, within 10 ns or less) time synchronized within each cluster, but inter-cluster time. The precision of synchronization may be fairly low or not required at all. Although precise synchronization times of 10 ns or less have been described for one particular embodiment, it should be noted that the predetermined synchronization time required to achieve an accurate position depends on the equipment used. This is very important. For example, in some wireless system devices that require an accuracy of 3 m for accurate position determination, the predetermined time may need to be 10 ns or less, but in other wireless system devices, a position accuracy of 50 m is sufficient. That may be the above. Therefore, the predetermined time is based on the desired precise position of the wireless system. The methods and systems of the present disclosure are for existing embodiments of tracking and locating DL-OTDOA and U-TDOA techniques that rely on geographically dispersed stand-alone (individual) transmitters and / or receivers. This is a significant improvement.

For example, in the DL-OTDOA technique, the relative timing difference between signals coming from an adjacent base station (eNB) is calculated and the UE position is in a network with a UE (handset) with or without UE support. Alternatively, it can be estimated within a UE (handset) with or without network assistance (SUPL-based only control plane or user plane). In DL-OTDOA, when signals from three or more base stations are received, the UE measures the relative timing difference between the signals coming from the pair of base stations and generates a hyperbolic position line (LOP). .. At least three reference points (base stations that do not belong to a straight line) are required to define the two hyperbolas. The position (position measurement) of the UE is at the intersection of these two hyperbolas (see FIG. 11). The position measurement of the UE is for the RF emitter (antenna) position of the base station. As an example, when using LPP (LTE Positioning Protocol, Release 9), DL-OTDOA localization is UE-assisted and E-SMLC (Evolved Serving Mobile Location Center) is server-based.

The U-TDOA technique is similar to DL-OTDOA, but its role is reversed. Here, the adjacent position management unit (LMU) can calculate the relative arrival time of the uplink signal arriving from the UE (handset) and estimate the UE position in the network without the assistance of the UE. .. Therefore, the U-TDOA is LMU-supported and the E-SMLC (Evolved Serving Mobile Location Center) is server-based. When relative arrival time values from three or more LMUs become available, the network's E-SMLC server generates a hyperbolic position line (LOP) and a UE position (position measurement) (see FIG. 27). The UE position measurement is for the LMU antenna position. In one aspect, unlike DL-OTDOA, in the case of U-TDOA, time synchronization of the eNB (of the base station) is not mandatory. Only LMUs require precise time synchronization for positioning. As an example, an LMU is essentially a computationally capable receiver. As a further example, the LMU receiver uses SDR (Software Defined Radio) technology. In a further example, the LMU may be a small cell, a macro cell, or a small cell type dedicated device that only receives.

Regardless of the embodiment, correlating the location of the SRS with respect to a particular UE, as provisioned by the network, allows UE identification and location. The location of the SRS may be done at the network level or within a local sector, such as a building DAS, a small cell, or a combination of small cells and macrocells that function in a particular area. If the location of the UE's SRS is unknown in advance, the solution may be able to correlate the location of the UE over the entire coverage area. By doing so, it will show the history of the UE's location. In some situations, it may be desirable to determine the location of a UE, even if the network does not indicate where to place the SRS for a particular UE. The position of the UE can be correlated with the SRS by determining the position of the UE or its proximity to a known position and can be correlated with the SRS transmitting the UE. Such location can be achieved by other location / proximity solutions such as Wi-Fi and Bluetooth. Users can also identify their UEs with a UE application or by walking to a predetermined location in order to identify them to the location solution.

11 and 27 show only macro base stations. In addition, FIG. 27 shows an LMU juxtaposed with a base station. Although these depictions are valid options, the LTE standard does not specify where the LMU can be placed, as long as the placement of the LMU meets the multilateration / three-range survey requirements.

In one aspect, a common deployment for indoor environments is a DAS (Distributed Antenna System) and / or small cell, which is an inexpensive base station that is highly integrated with RF. LMUs can also be placed indoors and / or in canvas-type environments, for example U-TDOA can be used in DAS and / or small cell environments. In another aspect, U-TDOA-based accurate indoor localization is achieved by a combination of an indoor-positioned LMU and an externally-positioned macrocell, for example, without the need for deployment of DAS and / or small cells. Or may have a smaller number of small cells. Therefore, the LMU can be deployed with or without DAS and / or small cells. In a further aspect, the LMU can be placed in an environment where cell signal amplifiers / boosters are used, and DAS and / or small cells are present or absent.

LTE Release 11 also contemplates the integration of LMU and eNB into a single unit. However, this imposes an additional burden on the time synchronization requirements between small cells when the individual small cell eNBs are geographically dispersed, allowing wireless / cellular service providers to be especially indoors and / or others. Not ready to meet these in an environment where GPS / GNSS is rejected.

DAS systems are essentially time-synchronized to a much higher degree (accuracy) than geographically dispersed macro / mini / small cells / LMUs. Using the DL-DTOA solution in a DAS environment alleviates the time synchronization problem, but in a DAS environment a single base station works for multiple distributed antennas, resulting in multiple antennas being the same. The same downlink signal is transmitted with the cell ID (identification number) of. As a result, the conventional DL-OTDOA approach does not work due to the absence of identifiable adjacent cells (antennas) that generate signals with different IDs. Nevertheless, as described in US Pat. No. 7,872,583, it is possible to use the DL-OTDOA technique with multipath mitigation processors and multipath mitigation techniques / algorithms. U.S. Pat. Extended use (these patents are incorporated herein by reference in their entirety). However, these matching algorithms are limited by the number of antennas that emit signals with the same ID. One solution is to reduce the number of antennas that issue the same ID, for example, dividing a large number of DAS antennas into two or more time-synchronized clusters with different IDs. Such a configuration increases the system cost (increases the number of base stations) and requires a handset / UE to support the above techniques.

Using U-TDOA in a DAS environment also adds to the costs associated with adding / installing LMU units. However, no changes to the UE (handset) are required and only the base station software needs to be upgraded to support the U-TDOA function. It is also possible to integrate multiple LMUs with a DAS system. Therefore, the U-TDOA method with LMU has many advantages when used in indoor environments, in campus environments, and in other geographically limited environments where GPS / GNSS is difficult.

Precise time synchronization between multiple geographically dispersed base stations and / or small cells, and / or between indoors and other LMUs in GPS / GNSS-denied environments, macrocells and / or outdoors such as GPS / It is more complex than the LMU equipment used in macrocells in a GNSS friendly environment. This is because the macrocell in the outdoor environment is in a high position and has an antenna installed outdoors. As a result, the GPS / GNSS signal quality is very good, and the transmission and / or LMU receiver of the macrocell antenna uses GPS / GNSS to a very high accuracy (standard deviation 10ns) over a sufficiently wide range. Can be synchronized.

In one aspect, indoors and in other GPS / GNSS rejected environments, time synchronization between multiple distributed base stations and / or small cells / LMUs is performed by multiple base stations and / or small cells and / or LMUs. Achieved by using an external synchronization source that produces a shared synchronization signal. This synchronization signal can be derived from GPS / GNSS, eg, 1PPS signal, and / or Internet / Ethernet networking, eg, PTP or NTP. The latter is a low-cost solution, but cannot provide the time synchronization accuracy required for accurate positioning. External sync signals derived from GPS / GNSS are more accurate (standard deviation up to 20 ns), but require additional hardware and installation requirements (eg wiring of these signals) and are more complex / expensive. be. Also, changes to the base station and / or small cell hardware / low level firmware may be required to adapt the external sync signal to a higher level of accuracy. Moreover, the standard deviation of 20 ns is not accurate enough to meet the 3 meter requirement, eg, the standard deviation of about 10 ns.

To overcome the above limitations, one embodiment uses an LMU device 2800 with multiple receiving antennas 2802 and signal channels 2804, as shown in the high level block diagram of the multichannel LMU of FIG. As an example, one or more signal channels 2804 may include signal processing components such as an RFE (RF front end) 2806, an RF down converter 2808, and / or an uplink positioning processor 2810. Other components and components may be used. In one aspect, the signal channel 2804 is juxtaposed within the LMU device 2800 and closely time synchronized (eg, standard deviation of about 3 ns to about 10 ns). In another embodiment, the antennas 2802 from each LMU signal channel 2804 are geographically dispersed (eg, similar to DAS). As a further example, external time synchronization components (eg GPS / GNSS, Internet / Ethernet, etc.) can communicate with the LMU device 2800. Precise time synchronization is more easily achieved within a device (eg, LMU device 2800) than by attempting to closely synchronize a large number of geographically dispersed devices.

As an example, when two or more multi-channel LMUs (eg, LMU device 2800) are deployed, time synchronization between these LMUs is relaxed, resulting in a large number of variances (using external source signals). A low-cost, low-complexity approach can be used to synchronize multi-channel LMUs. For example, Internet / Ethernet networking synchronization may be used, or common sensors (devices) may be deployed to provide timing synchronization between different multichannel LMUs.

The multi-channel LMU approach, on the other hand, reduces the number of hyperbolic position lines (LOPs) that can be used in determining position measurements, but overcomes this deficiency by improving time synchronization (see description and examples below). reference).

When using multilateration / three-range multilateration, UE positioning accuracy is based on the geometric dilution (GDOP) of accuracy due to the geometric placement of macrocell towers / small cells / _LMUs , and single ranging σ R_pseudo measurements. It is the following function of two factors: accuracy (see Gunter Seeber, Satellite Geometry, 2003).

GDOP is a function of the geographical distribution of the transmitting antenna (in the case of DL-OTDOA) or the receiving antenna (in the case of U-TDOA). For regularly placed antennas, the two-dimensional GDOP estimate is equal to 2 / √N (HBB LEE, ACCURACY LIMITED OF HYPERBOLIC MULTILATERATION SYSTEMS, 1973), and for cellular networks, N is "by the UE. The number of emitters (macrocell tower / small cell / DAS antenna) that are "audible" (in the case of DL-OTDOA), or the number of LMU / LMU receiving channels that can "listen" to UE uplink transmission (in the case of U-TDOA). ). Therefore, the standard deviation of the UE position error can be calculated as follows.

式中、σ_ＳＹＮＣは、外部時間同期信号の標準偏差（２０ｎｓ）である。
この場合（Ｎ＝８）、単一の測距測定値、及びＵＥ位置誤差σ_ＰＯＳの標準偏差は４．７４メートルに等しい。 Eight (indoor) single receive channel LMUs (regularly arranged) that are geographically dispersed detect UE uplink transmissions, and these LMUs have a standard deviation of 1 PPS signal (eg, 20 ns). ) Is assumed to be synchronized. In this case, there will be 7 independent LOPs with N = 8 and which can be used for UE position measurement. Further, it is assumed that the range of the distance measurement error standard deviation, σ _R, is 3 meters (about 10 ns). As a result, the accuracy of the single ranging measurement is as follows.

In the equation, σ _SYNC is the standard deviation (20 ns) of the external time sync signal.
In this case (N = 8), the single ranging measurement and _{the standard deviation of the UE position error σ POS} are equal to 4.74 meters.

As an example, if two 4-receive channel LMUs (eg, multi-channel LMU device 2800) with regularly arranged distributed antennas are detecting UE uplink transmissions, then each LMU will be three tightly packed. Generate a set of synchronized LOPs (eg, standard deviation of about 3 ns) and for 3 independent LOPs N = 4. In this case, two UE position measurements are generated, each with a standard deviation error of 3.12 meters σ _POS. Combining these two position measurements by averaging and / or other means / methods will further reduce UE position measurement errors. One estimate is that the reduction in error is proportional to the square root of the number of UE position measurements. In the present disclosure, this number is equal to 2, and the final UE position measurement error σ _{POS_FINAL} is 2.21 meters, which is acquired as 3.12 / √2.

In one aspect, some multi-channel LMUs (eg, LMU device 2800) have relaxed synchronization between these multi-channel LMUs and are used in indoor environments and other GPS / GNSS rejected environments. be able to. As an example, within a multichannel LMU device, LMUs can be closely synchronized (eg, standard deviation of about 3 ns to about 10 ns). In another embodiment, a small cell with a large number of single-channel small cells / LMUs and / or integrated LMU device electronics (LMU functionality embedded in the eNB) is a rack-mounted enclosure (FIGS. 31 and 32). And / or the fact that they can be clustered (eg, integrated, juxtaposed, etc.) within a cabinet (eg, 19-inch rack). The antennas of each single channel device can be geographically dispersed, similar to DAS. The devices in the cluster can be closely time synchronized (eg, standard deviation of 10 ns or less). Multiple rack-mounted enclosures can be synchronized for each communication requirement (eg VoLTE), thereby using a low cost and low complexity approach. Precise (tight) time synchronization between a large number of clustered (integrated) devices in a rack-mounted enclosure / cabinet is more than when a large number of geographically dispersed devices are closely time-synchronized. Easy to achieve and low cost.

In another aspect, multiple LMUs can be integrated with the DAS system, as shown in FIG. As an example, the LMU receiver can share the received signal generated by each DAS antenna, eg, a shared DAS antenna. The actual delivery of these received signals depends on the DAS embodiment (active DAS vs. passive DAS). However, an embodiment that integrates LMU and DAS shares the received signal generated by each DAS antenna having an LMU receiver channel and matches (correlates) each DAS antenna coordinate with the corresponding LMU / LMU receiver channel. ) Accompanied by producing Armanac. Again, using a clustering approach and / or multi-channel LMU is the preferred method for LMU and DAS integration.

Further, in the same manner, the received signal generated by each small cell antenna can be shared with the LMU receiver channel. Here, the time synchronization of small cells can be relaxed, for example, it is not necessary to meet the positioning requirement, but the LMU / LMU channel requires precise time synchronization. Using a clustering approach and a multi-channel LMU is the preferred method for LMUs for such options.

The integration of LMU and eNB into a single unit has a cost advantage over the combination of stand-alone eNB and LMU devices. However, unlike integrated LMU and eNB receivers, the standalone LMU receive channel does not need to process the data payload from the UE. In addition, the UE uplink ranging signal (SRS (sounding reference signal) in the case of LTE) is repeatable and time synchronized (to the serving cell), so that each stand-alone LMU receive channel has more than one. It can support an antenna, eg, two or more small cells (time-multiplexed). This can then reduce the number of LMUs (in small cells / DAS and / or other U-TDOA locating environments) and reduce the cost of the system (see also FIG. 28).

If the E-SMLC server of the wireless / cellular network does not have the required functionality for the DL-OTDOA and / or U-TDOA technique, this functionality will be available to the location server capable of communicating with the UE and / or LMU, as well as the wireless / cellular. It can be run by the network infrastructure and / or the location service server (see Figures 29 and 30). Other configurations may be used.

In another aspect, one or more LMU devices (eg, LMU2802) may be deployed with a WiFi infrastructure, eg, as shown in FIG. Alternatively, a listening device can be used to monitor the LMU antenna in the same way as the WiFi infrastructure. Therefore, the LMU device and / or the channel antenna serving the LMU can be juxtaposed with one or more WiFi / listening devices 3500, such as one or more WiFi access points (APs). As an example, the WiFi device 3500 can be geographically dispersed.

In one embodiment, the WiFi device 3500 may be connected to a power source. The RF analog portion 3502 (eg, circuit) of one or more LMU devices or channels can be integrated with the LMU antenna so that the RF analog portion 3502 can share power with the WiFi device 3500 (see Figure 35). ). As an example, the RF analog portion 3502 of an LMU device or channel may be cabled to an uplink locating processor circuit (eg, uplink locating processor 2810) that may include baseband signal processing. As a further example, such an embodiment is a signal-to-noise ratio (SNR) because signal amplification can be present between the antenna and the interconnect cable between the RF analog portion 3502 and the baseband circuit. Promote improvement. In addition, the RF analog portion 3502 can downconvert the received signal (eg, to the baseband) and the baseband signal frequency is several steps lower than the received signal in the antenna, which can ease cable requirements. Such relaxation of cable requirements can reduce connection costs and significantly increase transmission distance.

It should be understood that the ranging signal is not limited to the SRS, and other reference signals such as MIMO and CRS (cell-specific reference signal) can be used.

In a further embodiment, the 5G network-centric location approach can be improved by fusing the downlink OTDOA and uplink TDOA methods. For example, in a macrocell environment, the uplink TDOA method suffers from a lack of range. This is because the uplink signal transmission by the UE is orders of magnitude lower in power than the corresponding downlink signal transmission by the macrocell. As a result, the probability that the UE signal will be detected by the adjacent cell is low. For uplink TDOA, the 3GPP standard contemplates the deployment of LMUs that help improve the detectability of uplink signals (ie, LMUs are uplink transmitters and receivers with essentially additional signal processing capabilities. Is). Many LMU deployments address the issue of uplink signal detectability and allow multilateration positioning to locate the UE, but significant involvement with this deployment and many such devices. There is a cost. As a result, wireless network carriers did not deploy LMUs in the LTE 4G environment.

Therefore, it may be necessary to utilize the characteristics (property) of the serving antenna system for different UE positioning methods to enable uplink position identification in 4G and 5G environments. For example, depending on the system design of the serving cell antenna, an uplink method can be used to estimate the arrival direction (angle) (DoA / AoA) of the UE reference signal in the horizontal (also known as azimuth) plane. Theoretically, it may be possible to estimate DoA / AoA in a vertical (also known as elevation) plane. By combining DoA / AoA calculations in both horizontal and vertical planes, it may be possible to locate the UE. However, in practice, due to the design of the macrocell antenna, the accuracy of the DoA / AoA estimation for the vertical plane is too low for precise positioning, or the antenna elements in the vertical plane are inaccessible. DoA / AoA estimation cannot be performed at all.

Therefore, in order to measure the position using the uplink DoA / AOA method, it may be necessary to estimate the distance from the UE to the serving cell. This distance may be derived from round trip time (RTT) and time advance (TA) information based on measurements performed by the serving cell. However, these measurements may lack the accuracy required for radio wave propagation phenomena. One possible way to address the accuracy problem is to use the downlink OTDOA to estimate the distance between the UE and the serving cell. The disadvantage of this solution is that the downlink OTDOA requires accurate synchronization between the serving cell and the adjacent cell used for UE positioning, for example, the positioning accuracy is affected by the synchronization error. There is no alternative, as uplink TDOAs in the macro environment suffer from the same effects.

However, the uplink DoA / AoA does not have this error. This is because only serving cells are used, i.e. adjacent cells are not used and synchronization is not required. On the other hand, if the downlink OTDOA is used to determine the distance between the serving cell and the UE, this measurement may include the synchronization error described above. However, if the downlink OTDOA is used only to determine the initial UE position, the error can be reduced. The UE position is then state-of-the-art based on the UE's DoA / AoA and UE velocity (estimated from the same reference signal and / or other reference signal as DoA / AoA) measurements as long as the same serving cell is used. Can be calculated using the tracking algorithm of. Over time (ie, the number of measurements), the tracking algorithm or tracker will reduce the effects of synchronization errors. Since soft / softer handover between serving cells is not supported in 4G LTE, the initial UE distance measurement to the serving cell may need to be repeated each time the UE switches from one serving cell to another. Please note.

The tracking algorithm may be based on radar and sonar performance improvement strategies. A tracking algorithm or tracker may provide the ability to predict future positions of multiple moving objects based on the position and velocity history of individual objects reported by the sensor system. There are many different types of trackers, such as particle filter algorithms and Kalman algorithms. The use of trackers can also improve UE positioning when the initial UE position is calculated based on the RTT measurements described above. The choice of whether to measure the distance from the UE to the serving cell using the downlink OTDOA or RTT / TA depends on the magnitude of the error in the distance estimation. The greater the error, the greater the impact on tracker performance. Further, after the handover, it takes a certain amount of time to estimate the RTT by the network. During this time, the reported RTT measurements may not be valid. In one embodiment, two different independent trackers are used simultaneously, using a position that converges in a shorter time (ie, determines the position first). In yet another embodiment, the downlink OTDOA is used to estimate the distance and the tracker is used to estimate the distance for each position measurement to correct the synchronization error. This approach can provide the best accuracy.

As mentioned above, the uplink method is network-centric in nature, and the associated UE reference signal transmission is an LMU integrated with eNodeB in an eNodeB and / or macro environment, or a stand-alone LMU, or a DAS system in other environments. It may be collected and preprocessed by an LMU integrated with, etc., and then transferred to the LSU for further processing to determine the UE location using one or more network protocols. In the case of downlink OTDOA, the task of collecting and preprocessing the downlink reference signal is performed by the UE. The UE then sends the collected downlink data to the LSU. The UE uses the control plane and / or the LTE user (data) plane to handle communication with the LSU. Therefore, signaling may be in accordance with the OMA Secure User Plane Localization (SUPL) protocol and / or 3GPP, such as the LTE Positioning Protocol (LPP).

A large number of uplink and downlink reference signals may be used for UE positioning. The most commonly used uplink positioning is a sounding reference signal (SRS) and / or a demodulation reference signal (DMRS). The most commonly used downlink positioning is a positioning reference signal (PRS) and / or a cell-specific reference signal (CRS). The reference signal may be collected in digital form, i.e. a sample, which can be preprocessed before being transmitted to the LSU. The digital sample of the reference signal may be extracted from the baseband I / Q sample in the time domain or the baseband I / Q sample in the frequency domain, as well as the resource element (RE) from the OFDM symbol. The resource element is a complex coefficient representing one OFDM subcarrier for the duration of one OFDM symbol. Therefore, RE can represent an LTE symbol in the frequency domain. The I / Q value represents the in-phase (I) component and the orthogonal (Q) component of the signal. As a result, the reference signal can be represented by an I / Q sample in the time domain, or an I / Q sample in the frequency domain, or a symbol RE containing the reference signal.

In the downlink OTDOA UE embodiment, both the PRS reference signal and the CRS reference signal can be collected. In other words, the method may use PRS, CRS, or both types of signals. This hybrid operating mode (ie, PRS or CRS, or both PRS and CRS) has the advantage of allowing the network operator to dynamically select the operating mode depending on the situation or specific network parameters. For example, PRS may have better listening ability than CRS, but the use of PRS may reduce data throughput. CRS does not affect throughput and has a higher reference signal density. This is advantageous when moving the UE. In addition, the CRS is backwards compatible with all previous LTE releases, such as Release 8 and below. Thus, the hybrid method provides network operators with the ability to make trade-offs, or balance, between audibility, throughput, compatibility, and accurate positioning / tracking of moving targets.

In one embodiment of the downlink OTDOA, the UE receiver is configured to detect and separate a number of OFDM symbols carrying reference signals from one or more downlink frames, such as PRS and / or CRS. It may include a detector. The detector may be further configured to extract a resource element (RE) (see FIG. 9) from an OFDM symbol for each symbol, and a resource element (RE) from a large number of OFDM symbols for each symbol. May be configured to collect and store, that is, for each symbol ID (ID), to generate a downlink data structure containing a RE that correlates with this symbol ID. In addition, the detector may be configured to collect downlink metadata including each frame start and other related and / or auxiliary information.

In one embodiment, the exemplary data structure of the OFDM symbol containing the CRS RE element is:
-CRSR_data_sruct: [Number of radio slots captured within the capture block, time interval between capture blocks (number of slots), CRS data length, and CRS data (resource element)].

In one embodiment, exemplary metadata transmitted from the UE to the LSU includes:
-Serving cell information: [physCellId, cellGlobalId, CellGlobalIdEUTRA-AndUTRA, earfcn-DL, systemFrameNumber, slot number, UTC time stamp, rsrp-Result, rsrq-Result,
-Ue-RxTxTimeDiff, DownlinkPathLoss, Bandwidth (in Physical Resource Block (PRB)).
-Adjacent cell information (0 to 32 adjacent cells): [physCellId, cellGlobalId, CellGlobalIdEUTRA-AndUTRA, earthcn-DL, systemFrameNumber, rsrp-Result, rsrq-Result.
-UE information: [UE ID, UE category, mobility, MobilityHistory Report].

In one embodiment of the downlink OTDOA, the UE receiver sends signals to the LSU, such as being configured to send RE data and metadata to the LSU to receive commands and support information, and to send the downlink data. It may include a communication processor configured to be exchanged. Note that signaling may be in accordance with the OMA SUPL protocol and / or 3GPP LPP, or a combination of LPP and SUPL. In addition, dedicated interfaces and / or protocols may be used.

In one embodiment of the downlink OTDOA, the detector of the UE receiver may be configured to extract a reference signal I / Q sample in the time domain from the OFDM symbol for each symbol, and for each symbol. Collect these I / Q samples in the time domain from a large number of OFDM symbols, i.e. for each symbol ID, generate a downlink data structure containing the I / Q samples in the time domain associated with this symbol ID. May be configured in. In addition, the detector may be configured to collect downlink metadata including each frame start and other related and / or auxiliary information.

In one embodiment of the downlink OTDOA, the detector of the UE receiver collects I / Q samples in the time domain from a large number of OFDM symbols per symbol, ie, for each symbol ID, associated with this symbol ID. It may be configured to generate a downlink data structure containing I / Q samples within a certain time domain. In addition, the detector may be configured to collect downlink metadata including each frame start and other related and / or auxiliary information.

In one embodiment of the downlink OTDOA, the detector of the UE receiver collects RE from a large number of OFDM symbols for each symbol, i.e., for each symbol ID, a downlink that includes a RE associated with this symbol ID. It may be configured to generate a data structure. In addition, the detector may be configured to collect downlink metadata including each frame start and other related and / or auxiliary information.

The OFDM symbol carrying the reference signal RE may include both the payload and the reference signal RE. As a result, the RE collected from the OFDM symbols by the detector, and thus the downlink data structure generated from these collections, contains both the payload and the reference signal RE. When this data is sent to the LSU, the payload RE becomes overhead and reduces the uplink capacitance.

The positions of the CRS reference signal RE and the PRS reference signal RE in the frequency dimension of the symbol may be determined by the cell ID, antenna configuration, antenna port, slot number in the radio frame, and OFDM symbol number in the slot (3GPP 36. See also 211 v13 or ETSI TS 136 211 V13.0.0). If this information is known to the UE receiver detector, the detector is configured to remove the payload RE, thereby reducing the size of the downlink data sent to the LSU, i.e. reducing the overhead. May be done. The amount of reduction can vary depending on the type of reference signal, adjacent cell ID, and other cell parameters. For example, in the case of CRS, the reduction in data size may be three times (for cells with dual antenna sectors). In other cases, it may not be significantly reduced, but rather rare. This is because the position of the reference signal RE in the frequency dimension of the symbol is repeated based on the [(cell ID) mod6] reference. Therefore, for CRS, on average, the reduction in data size can be about 40% compared to the worst.

Further reductions in data size can be obtained by compressing the size and phase of the complex RE to fewer bits. There are many compression algorithms. Some are well known, such as A-law and u-law companding algorithms. Other algorithms target the reduction of baseband unit (BBU) pool and remote radio unit (RRU) connection bandwidth in the C-RAN (Centralized, Coordinated Cloud Radio Access Network) architecture. Note: In the C-RAN architecture, the baseband unit (BBU) is located in the center of the pool connected to the remote radio unit (RRU) via optical fiber. In addition, there are companding algorithms used in radar technology.

In one embodiment, the detector of the UE receiver is configured to compress a 16-bit to 32-bit RE size to an 8-bit RE size, for example, to reduce the data size by a factor of 2-4. ..

In one embodiment of the downlink OTDOA, the LSU is configured to process downlink reference signal data and other related and / or auxiliary downlink information such as frame initiation transmitted from one or more UEs. .. This process involves searching for all detectable (meeting certain criteria) reference signal emitters in the located database / list. This process is also performed for each antenna, eg, for the antenna port of the LTE device and / or for each associated LTE network component. The output of the LSU is the position of one or more UEs and other downlink position related metadata (reliable radius value, FCC NG911 position accuracy metric, etc.).

The LSU is a downlink signal processor configured to estimate the arrival time (ToA / TDOA) and / or flight time (ToF) of the reference signal from the downlink reference signal data and other information transmitted by the UE. May include. The processor is also configured to determine the time difference between a reference cell and an adjacent cell (measurement cell) (ie, RSTD / TDOA (RSTD means reference signal time difference)). The downlink signal processor uses positioning signal processing algorithms as well as other techniques and techniques including multipath mitigation algorithms and methods such as advanced spectrum estimation algorithms, constant alarm rate (CFAR) detection algorithms, and spatiotemporal adaptive processing (STAP). May include. In addition, the downlink signal processor may be configured to estimate the carrier frequency offset (CFO) using one or more specific algorithms and / or techniques, which causes the LSU to move one or more. It may be possible to track the UE to be used and reduce the clock frequency mismatch between the transmitter (cell) and the receiver (UE). The CFO estimate is used to update (correct) the downlink reference signal data.

Most cell towers use MIMO antennas. Each cell tower sector has a MIMO subsystem consisting of multiple (two or more) antennas. These antennas are completely coherent. That is, the time and phase are synchronized. To avoid interference between reference signal transmissions, a) the gold code seed (used to encode / generate the reference signal) is different for each antenna, and b) different resource elements (subcarriers) are for the antenna. Assigned for each reference signal transmission, c) when one antenna from the MIMO subsystem of the sector is transmitting the reference signal, transmissions from the other antennas are muted. As a result, the UE receiver can detect (discriminate) the transmission of the reference signal from the subsystem antenna of each sector.

The most commonly deployed today are sector antenna subsystems with dual antennas, and like any MIMO antenna subsystem, these antennas are spatially separated (approximately 6 feet). However, it will be appreciated that the sector MIMO subsystem is not limited to two antennas and can have varying antenna separation distances. A conventional method of selecting a reference signal from different antennas in the same sector is to select the antenna (signal) with the highest SNR (signal-to-noise ratio) and / or SNIR (signal-to-noise + interference ratio). However, in many cases, these criteria do not guarantee detection of the direct line of sight (DLOS) or the direct path of the reference signal, and the UE position measurement is determined from the reflected signal. In this case, that is, the DLOS / direct path is not detected and the position accuracy is affected. Therefore, it is necessary to detect (DLOS) or direct path signals for accurate positioning.

Since the wireless network is a terrestrial system, the DLOS path is often blocked to varying degrees, and the DLOS signal strength is often significantly lower (15 dB or more) than the reflected signal strength. At the same time, the wireless network is a terrestrial system, and even if the DLOS is severely blocked (> 15 dB) due to RF propagation phenomena (surface waves, Fresnel waves, etc.), from the sector antenna to the UE receiver. There is always a direct path for the RF signal. This direct path is somewhat longer than the DLOS path, but much closer to the DLOS path length than the reflective path. That is, the influence on the positioning accuracy is minimal.

The spatial separation of the sector antenna is at least two orders of magnitude smaller than the distance from the UE to the sector antenna. As a result, the signal propagation paths of each antenna are very close and should experience similar attenuation. However, the spatial separation of the sector antennas is large enough for the reference signal from each sector antenna to experience the multipath phenomenon, affecting the DLOS / direct path signal strength as well as the reflected path signal strength. Multipath interference can be strengthened or weakened, i.e., the signal can be amplified or attenuated, and due to the antenna spatial separation signal of the sector from each antenna, i.e. impact is affected differently. Depends on the antenna.

When comparing signals from two antennas in the same sector, the reference signal from the first antenna may have an amplified reflected signal, while the DLOS / direct path signal may be attenuated, eg, The reflected signal power can be significantly higher (> 20 dB) than the DLOS / direct path signal power. The reference signal from the second antenna may have a higher (eg 3 dB or 4 dB) DLOS / direct path signal strength and a reduced (eg 5 dB lower) reflected signal as compared to the first antenna. .. At the same time, the DLOS / direct path signal power of both antennas is much lower than the reflected signal power (in some cases> = 10 dB). As a result, the signal with the higher reflected signal power, for example from the first antenna, has a higher SNR / SNIR and the conventional approach to determine the UE position measurement (selecting the reference signal from the antenna). Is selected (using). However, the DLOS / direct path signal power of the first antenna may fall below the detection threshold and affect the position accuracy. Subsequently, in order to increase the probability of DLOS / direct path detection, a signal from the second antenna having a higher DLOS / direct path signal strength (3 dB to 4 dB) is selected.

In one embodiment of the LSU downlink signal processor, ToA / ToF results are evaluated (compared) for each antenna in a given sector to determine the DLOS / direct path. By definition, DLOS / direct path represents the shortest distance between the UE and the tower with respect to the reflection path. Therefore, the DLOS / Direct Path has the shortest ToA or the lowest ToF. Based on the above description, the possible results when comparing ToA / TOF results from a pair of antennas are as follows.
1. 1. The signals from both antennas result in the same shortest ToA value and / or minimum ToF value.
2. The shortest signal ToA and / or minimum ToF value from one antenna is smaller than the shortest ToA value and / or minimum ToF value from another antenna.
In the first case, antenna (signal) selection is based on reliability metrics (see below). In the second case, an antenna (signal) with a lower ToA / ToF value is used to calculate the position measurement, provided that this signal meets the reliability metric parameter threshold requirement. Note: This reliability metric is needed to avoid (mitigate) false positives (determinations) of DLOS / direct paths caused by false alarms, such as noise and / or interference above the detection threshold.

If the antenna subsystem of the cell tower sector consists of three or more antennas, an iterative process is used, a) in step number 1 a large number of antenna pairs are formed and these pairs are evaluated, b) in step number 2 , A subset of antenna pairs is formed from the remaining antennas, each antenna pair is evaluated, c) until the pair cannot be formed, i.e., only one antenna (signal) is left (available). Is repeated.

In addition, the downlink signal processor is configured to calculate the TOA reliability metric for each ToA / ToF from the antenna of each cell and each cell sector. This calculation may include whether the total signal strength and / or the SNR / SNIR of the received signal meets the desired threshold and ToA / ToF signal statistics such as standard deviation, mean absolute deviation (MAD). Whether or not the direct path / DLOS is found, and if the direct path is identified, may include the SNR / SNIR of the direct path / DLOS. Additional information may include whether the serving cell is the closest cell, whether the serving cell has the highest SNR / SNIR, and an accurate geometric dilution (GDOP) calculation for each combination of RSTDs. Note that the GDOP depends on the geometry of the cell position and the orientation (in the azimuth plane) of the sector's antenna subsystem. GDOP may indicate the effect of this geometry on the final UE position estimation. The GDOP value may depend on the angle at which two given RSTD / TDOA lines intersect. In the best case (GDOP = 1), this angle is 90 degrees. In the worst case (GDOP> 20), the angle is small. The RSTD / TDOA hyperbola is sometimes referred to as the position line (LOP).

As with the cell sector antenna subsystem, the UE may include multiple (two or more) antennas and associate these antennas with multiple receiving channels. Any of these antennas may receive a reference signal, thereby giving the UE the option of collecting incoming signals from each UE antenna. The reference signal from each antenna may be collected by the UE, preprocessed, and then transmitted to the LSU, as in the downlink OTDOA UE embodiment described above. The UE antennas may be in close contact to reduce variations in the effects of multipath between the antennas. However, UE antennas may be designed for polarization diversity. As a result, the above-mentioned antenna (signal) selection determination flow (algorithm) repeats the sector antenna selection algorithm for each UE antenna, and then uses the above-mentioned sector antenna selection algorithm to select among the remaining candidates. By doing so, it is extended.

Embodiments of LSU may include a downlink signal processor that compares results between ToA / ToF results from each antenna in a given cell / sector. The downlink signal processor may also compare the results between each UE antenna as described above. The rationale for such a comparison is that the antenna polarization phenomenon can attenuate some interference and / or reflection paths and at the same time amplify the DLOS / direct path signal. In other words, the information redundancy associated with such a comparison can increase the probability of DLOS / direct path detection.

The downlink signal processor is also configured to estimate the carrier frequency offset (CFO). There are two main causes of CFO. The first cause is the Doppler shift, which is the result of the relative movement between the transmitter (cell) and the receiver (UE) in the mobile environment, and the second cause is the UE after the down-conversion process. A clock frequency mismatch between the transmitter (cell) and the receiver (UE) that results in residual CFO at the receiver. Since the loss of orthogonality reduces the communication performance of the OFDM system, CFO estimation is required to maintain / maintain the orthogonality of the subcarriers. Similarly, when the OFDM reference signal is used as a ranging signal to locate the UE, i.e., when determining the ToA / TDOA and / or ToF time, the subcarrier frequency offset is the arrival time (ToA / TDOA). And / or affect the estimation accuracy of flight time (ToF). Therefore, to mitigate the effects of UE movement and / or clock frequency inconsistencies, downlink signal processors are used to modify reference data and UEs for TOA / TDOA and / or ToF estimation accuracy. Perform CFO estimation to mitigate the effects of movement and / or clock frequency offset. It should be noted that while the UE receiver synchronizes with the serving cell and calculates the CFO for the serving cell, it needs to modify the CFO from multiple adjacent cells to estimate the ToA / TDOA and / or ToF used for UE positioning. Please note that there is. Here, the Doppler shift differs from cell to cell depending on the direction of movement of the handset with respect to the cell position. Also, the clock frequency mismatch between each cell and the receiver (UE) is cell dependent. Furthermore, for accurate positioning, these CFOs should be estimated with higher accuracy than for communication purposes.

In LTE and other OFDM-based systems, the CFO can be estimated using either the time domain method or the frequency domain method. In the time domain, the cyclic prefix (CP) method and the training sequence method are commonly used. The frequency domain estimation method can be further classified into the training chimbuol method and the pilot method. Both the training sequence and training symbol methods require a dedicated training sequence or training symbol that does not exist (is not transmitted) within the LTE frame or symbol structure. If one of these approaches is used, LTE frame / symbol format changes will be required, affecting implementation on existing mobile wireless networks. On the other hand, CP and pilot (also known as reference) signals are part of the LTE frame or symbol structure. More accurate CFO estimates are generated compared to CP-based CFO estimates and pilot-based methods. Also, time domain CP-based estimation cannot be used in embodiments where resource elements are collected by the UE and sent to the LSU to determine position measurements. This is because the RE dataset does not contain CP data. Therefore, an embodiment of our CFO estimation uses a plurality of pilot signals in the LTE (OFDM) frame, that is, a reference signal.

In one embodiment of CFO estimation, the CFO is estimated in the frequency domain and the reference signal is compensated by the estimated CFO in the time domain. In this embodiment, it consists of an FFT of all slot reference signal subcarriers in the frequency domain. CFO estimation is performed by searching for peaks in the two-dimensional space formed in the frequency domain. See the process of the CFO estimation process below.

In this embodiment, a single LTE frame is used to determine the CFO. However, a single frame is not a constraint, as two or more frames and frame fractions, such as 10 slots (half frames), can be used. In the LTE frame, there may be no reference signal for all symbols. See the example of the CRS signal in FIG. On the other hand, from FIG. 9, each LTE frame slot has the same distributed CRS signal, so that the CFO estimation can be performed slot-based.

The following is a description of the process of estimating the CFO. This process applies to individual reference signals from each of the serving cell and the audible adjacent cell.

Step 1: Demodulate the RE of the reference signal using a matching filter in the frequency domain.

Step 2: When using the CRS, the demodulated CRS samples in each slot (on the CRS subcarrier) are combined to produce multiple combined CRS signals per slot, ie, with a period (interval) of 0.5 ms. Alternatively, it produces 20 signals per frame for a period of 10 milliseconds. Note: From the above description, the coupled CRS sample sequence within each slot is in the frequency domain.

Step 3: The Inverse Fast Fourier Transform (IFFT) is applied to all slot CRSs (CRS subcarriers in the frequency domain) to generate a CRS sequence slot by slot in the time domain. Therefore, at the end of this step, there are 20 CRS sequences each in the time domain. The number of elements in all CRS sequences (in the time domain) is the same and is equal to the duration of the CRS signal divided by the ADC sampling rate (in the time domain). The number of elements in the CRS sequence is N, and for all n, n is 1. .. .. .. .. .. Assuming that it belongs to, N, it is possible to form a sequence of 20 elements. In such an "n" sequence, each element is from a different slot.

Step 4: The Fast Fourier Transform (FFT) is applied for each "n" sequence of 20 elements to generate a total of N sequences with each of the 20 elements in the frequency domain.

Step 5: The space of N × 20 elements in the frequency domain is searched for the peak, and the CFO is calculated from the maximum value of this peak.

The accuracy of determining the maximum peak value, and thus the CFO estimation accuracy, is limited by the time interval between the slot and the frame period. Therefore, in order to improve the accuracy in this embodiment, an interpolation algorithm is used when finding the maximum value of the peak.

In another embodiment, in step 4, advanced spectral estimation algorithms such as matrix pencils, MUSIC, and ESPRIT replace the FFT operation. These algorithms allow for further improvement in peak maximum determination accuracy.

Further, a) other reference signals can be processed in the same manner, b) the reference signals may be combined other than slot by slot, and c) the reference signal can be used for each symbol. It should also be understood that binding may not be required.

In the LSU embodiment, the output (result) from the downlink signal processor, such as ToA / ToF value, RSTD / TDOA value, reliability metric, DLOS / direct path probability, estimates one or more UE positions and has a confidence radius. It is passed to a navigation (position) processor configured to generate other downlink position-related metadata such as values, FCC NG911 position-specific accuracy metrics.

In a downlink OTDOA UE positioning embodiment, the LSU may include a navigation processor using a multilateration technique or method also known as hyperbolic navigation. Hyperbolic navigation is based on the timing difference, ie RSTD / TDOA, and does not refer to the common clock. Navigation processors also use the above information redundancy, such as TOA / TOF, reliability metrics, DLOS / direct path probabilities, to reduce ambiguity in multilateration position measurements and apply position matching algorithms. It may be configured to do so.

Multilateration techniques and methods involve solving a large number of hyperbolic (RSTD / TDOA) equations, and a number of different algorithms / approaches can be used to find the correct solution. Some algorithms / approaches include an initial estimation of the target (UE) position, an iterative method that can be initiated by "guessing". Estimates can then be improved at each iteration by determining the position solution of the local linear least squares method. One drawback of this approach is that the initial position estimation needs to be very close to the final position solution to ensure the convergence and / or absence of local values that can lead to significant position errors. On the other hand, it can work well in overdetermined situations where there are more measurement equations than unknowns. In this regard, it should be noted that the overdetermined situation reduces the possibility of ambiguous and / or unrelated solutions that may occur if only the minimum required measurements are available.

There is also a non-repetitive solution to the problem of hyperbolic position estimation. These solutions are closed form and can be valid for both distant and near sources, thereby eliminating the convergence and / or minimal problems of the iterative approach. One drawback to the non-repetitive solution is that it requires prior knowledge of the approximate position to be determined, such as the distance between the UE and the first cell, eg, the serving cell. Another drawback of non-repetitive solutions is that they are closed-form solutions that are not designed for overdetermined situations. Nevertheless, it may be possible to make the non-repetitive solution work in the context of overdetermined systems by using additional variables to transform the original TDOA nonlinear equation set into another set of linear equations. For example, a weighted linear least squares algorithm provides an initial position solution, and then a second weighted least squares uses known constraints and additional variables in the source coordinates to improve position / position estimation. Provide a value.

In one embodiment, when an overdetermined situation occurs, multiple sets of 3RSTD / TDOA subsets are formed. Then, for each subset, a closed form solution is found. The position measurement is then finalized using a position matching algorithm.
In another embodiment, position measurements are found by a combination of iterative and non-repetitive solutions from the same set of RSTD / TDOA values.

From the above, the iterative and non-repetitive approaches need to have accurate initial estimates of UE position. This estimation can be improved by time advance (TAV or TA) (also known as RTT) information. Timing advance is used to compensate for propagation delay as the signal travels between the UE and the serving cell tower. The base station of the serving cell allocates TA to the UE based on the measured distance of the UE (see FIG. 37).

The LTE Timing Advance Type 1 measurement (see FIG. 38) corresponds to a round trip time, i.e., a signal reciprocating propagation delay. Timing advance propagation delays may be from DLOS / direct paths or reflection paths and include propagation delays from cell tower cables and base stations / UE electronics. In addition, the UE adjusts the transmission timing with _{four times the T s accuracy (T s} is the LTE system timing, which is equal to 32.55 ns).

Type 1 is defined as the sum of the transmission / reception timing difference in the eNB and the transmission / reception timing difference in the UE as follows.
TADV = (eNB Rx-Tx time difference) + (UE Rx-Tx time difference).
Therefore, the distance d to the base station is estimated using the following equation.
d = c ^* (TADV / 2) (in the formula, c is the speed of light) or d □□ c □□ (RTT / 2) (in the formula, c is the speed of light).

TA (RTT) is available from the serving cell and represents a UE range estimate independent of the serving sector. However, TA cannot be obtained from the UE. Instead, the UE provides a transmit / receive timing difference, i.e., access to the UE Rx-Tx. From the above, UE Rx-Tx = RTT-eNB Rx-Tx. On the other hand, from FIGS. 37 and 38, when the UE TA is adjusted, the eNB Rx-Tx time difference of the serving cell becomes the same for all UEs. As a result, UE Rx-Tx measurements still correspond to RTT, but with a bias that depends on the cable length of the cell tower antenna and the electronics of the base station.

In one embodiment, the propagation delay of the antenna cable length can be estimated from the height of the tower, and the propagation delay of the base station electronics can be estimated from the statistical data collected from the various towers. ..

If the RTT is known, the UE may be located along an arc defined by the azimuth beamwidth of the serving sector and the RTT / 2 range (also known as the radius). Since the azimuth beamwidth of the serving sector can be a large value reaching 120 degrees, the arc length increases rapidly with that distance, affecting the estimation accuracy of the initial UE position. However, the accuracy of position / position estimation can be improved by the fact that the UE can also be positioned by the intersection of the azimuth beamwidths of the serving sector and adjacent cell sectors. This approach helps mitigate the effects of arc expansion. Further improvements also include the mechanical downtilt angle and / or electrical downtilt angle of the sector antenna, antenna gain, elevation beamwidth (in addition to azimuth beamwidth), and cell tower height and tower structural type. Can be achieved by considering.

LTE does not support soft UE handover, but in many cases the UE is switched between two or more adjacent cells, even if the UE is in steady or quasi-steady state. In addition to signal propagation disturbances, this serving cell switching can be the result of efforts to equalize the eNB load of the wireless network.

In one embodiment, these frequent serving cell handovers are used to estimate RTT values from two or more geographically diverse serving cells. As mentioned above, the UE may be located along the arc defined by the azimuth beamwidth of the serving sector and the RTT / 2 range. Therefore, there are two or more such arcs, defined from two or more geographically diverse serving cells, and the position of the UE is determined at the intersection of these arcs. Note that due to the inherent RTT estimation error, multiple intersections may or may not be present at all. However, the information about redundant arcs further improves the RTT-based position measurement described above, which is also used in this embodiment.

To develop even more sophisticated initial UE position estimation, the LSU navigation processor may be configured to work with the LSU uplink signal processor. Like the LSU downlink signal processor, the LSU uplink signal processor receives uplink reference signals, such as SRS and / or DMRS, data collected from one or more UEs and preprocessed by the eNodeB (cell). You can. In eNodeB, a digital sample of the uplink reference signal is extracted from the baseband and collected as the uplink reference signal along with the relevant uplink metadata. The UE uplink data and associated uplink metadata are then transmitted to the LSU uplink signal processor. The uplink signal processor will determine the AoA / DoA observable based on the UE uplink data and associated uplink metadata, as well as the known configuration / parameters of the eNodeB sector antenna array contained in the uplink metadata. It may be configured. The AoA / DoA observation is then transmitted to the LSU navigation processor to reduce the ambiguity of the observation and generate the AoA quadrant azimuth line (LOB) and / or direction of arrival (DoA) from the AoA observation and uplink metadata. good.

In one embodiment, the AoA / DoA estimates generated by the LSU navigation processor dramatically limit the expansion of the arc relative to the distance from the eNodeB (serving sector). This is unlike the sector azimuth beamwidth, which can reach 120 degrees, where the AoA quadrant azimuth line (LOB) and / or arrival direction (DoA) error estimated by the LSU navigation processor is approximately 1 degree or less. Because. Therefore, the arc expansion when using the LSU uplink signal processor and the LSU navigation processor can be less than 100 times compared to the conventional use of sector azimuth beamwidth. As a result, the accuracy of the initial UE position estimation can be increased by a factor of 100. Therefore, AoA / DoA estimation can improve the downlink OTDOA UE positioning accuracy of the navigation processor, and this more accurate downlink OTDOA UE positioning is then the uplink AoA / DoA / UE positioning of the navigation processor. Can be improved. As a result, an embodiment using UE positioning that combines uplink / downlink or downlink / uplink becomes possible.

In a wireless network environment, it may not be possible to use the multilateration method, which requires at least three reference points (in the case of 2D position identification) to obtain the UE position. If four or more reference points can be used, 3D positions can also be extracted. Furthermore, if the signal is received by a MIMO antenna, the orientation of the UE can be established. If vertical snapshot information is available from the MIMO antenna, the elevation angle of the UE can also be determined. For example, some high-density urban environment wireless networks use only two high-power cell towers that deliver large amounts of RF signals over a wide area. DLOS cannot be used in this environment, but the effect of the absence of DLOS is small. This is because for data communication purposes, communication can be performed using the reflected signal as long as the reflected path delay is less than the cyclic prefix (CP) length. This creates problems with common navigation techniques that rely on at least three detectable reference points.

In one embodiment, UEs located within a two-tower / cell environment are further shown in FIG. The method may be initiated by plotting the TDOA hyperbolas of two detectable cells / tower sectors 3602 and 3604, such as the hyperbolas 3606 and 3608, and finding the hyperbola to which the target UE belongs. The hyperbola of the UE can be determined by finding the sector azimuth beams from each of the cells / towers 3602 and 3604 in which the UE resides. A hyperbola that belongs to both sector azimuth beams is selected. As shown in FIG. 36, the cell / tower 3602 has a sector azimuth beamwidth 3610 and the cell / tower 3604 has a sector azimuth beamwidth 3612. In the example of FIG. 36, the sector azimuth beamwidths are the same and equal to 60 degrees. The intersection 3616 between the sector azimuth beamwidth 3610 and the sector azimuth beamwidth 3612 is the region with the highest probability of the UE being located. The hyperbola 3608 belonging to this region with the highest probability is the hyperbola selected.

If TA or UE Rx-Tx (also known as RTT) measurements from at least one sector (tower) are available, the UE is determined by the azimuth beamwidth and RTT / 2 range of the serving sector, eg, the radius of the arc. It may be located along the arc to be. Therefore, the UE may be positioned near the hyperbola and arc intersection. Note: Due to the inherent RTT estimation error, multiple intersections may or may not exist at all. However, redundant arc information further enhances RTT-based position measurements.

A UE belongs to the intersection of LOBs and hyperbolas if AoA / DoA estimates from at least one sector (tower) are available. This solution can be the most accurate.

In addition, if neither RTT nor AoA / DoA estimates are available, the UE position measurement scores one or more heuristic approaches, eg, intersections corresponding to each cell / tower (sector) on the selected hyperbola 3608. It can be determined by ringing. The score may be based in part on the cosine of the difference in angle between the direction in which the cell / tower is facing and the direction of the point on the hyperbola tested from the cell / tower sector. As shown in FIG. 36, the cell / tower 3602 points in the direction 3620 and the cell / tower 3604 faces the direction 3622 that defines the intersections 3630 and 3634. The rest of the score may be obtained from the distance from each point 3630 and 3634 to the corresponding cell / tower. Once the scores for each of the points 3630 and 3634 are determined, the point with the highest score can be determined from cell / tower 3602 and cell / tower 3604. The points with the two highest scores on the hyperbola are then weighted according to their corresponding cell / tower SNR. Finally, the point between the two highest scored points (closer to the higher weighted point by SNR) is selected as the UE position measurement. As shown in FIG. 36, when the true position of the UE is position 3652, the UE position 3650 is estimated.

Returning to the discussion of LSU embodiments, the LSU may further include a communication processor configured for signaling and information exchange with the UE, an eNodeB, and network elements. Signaling conforms to the OMA SUPL protocol and / or 3GPP LPP / LPPa, or a combination of LPP, LPPa, and SUPL, and other protocols used or may be used to communicate with the network, such as the LCS-AP protocol. You can do it. In addition, dedicated interfaces and / or protocols may be used.

Examples of information exchanged in this way can be:
-Site name:
Technology (ie 4G, 5G), active (eg Y / N) (ie radio), in-building (eg Y / N);
Global cell ID, PCI value, frequency, IsGPS synchronized, DL Tx configuration (ie, number of Tx ports, maxTx power, DL bandwidth);
Tower structure type (ie rooftop, monopole, side of building, etc.), cable length and loss; antenna type (ie omni, directivity), latitude, longitude, antenna altitude AGL (ie above the surface), Tower base altitude MSL (ie, average interface), geoid;
Antenna azimuth, elevation, mechanical down-tilt, electrical down-tilt, gain, H-beam width, V-beam width;
Cell bandwidth, TA, list of adjacent cells, PRS configuration;
Configuration of eNodeB sector antenna array.

Rethinking UE power consumption in IoT applications, LTE modems that support IoT may pursue further power reduction options. For example, one option for UEs is to send ranging signal data only when the UE is already connected (to the network). Another option for the UE is that if the desired conditions are met to achieve high position accuracy, for example, the number of towers detected is greater than the threshold number N and / or the SNR / SNIR value is at a particular level. The distance measurement signal data is transmitted only when the value exceeds. Note: This approach cannot be applied when instantaneous position measurements are required, but can be acceptable for tracking UEs in motion (trajectory determination), where some delay is acceptable.

In LSU embodiments, all LSU components / elements (communication processors, downlink signal processors, uplink signal processors, navigation processors, etc.) are implemented in software that can run on one or more network core elements. You can. In one embodiment, these LSU components can also be run on an evolving 4.5G MEC (Mobile Edge Computing) server at the edge of the facility, thereby, for example, the LSU component is 4.5G. It may be integrated as a hosted application on MEC.

In another embodiment, the LSU components in the 5G deployment may be hosted within the core network computing cloud. In this embodiment, the LSU hosted within the core network computing cloud may support Infrastructure-as-a-Service (LaaS) data delivery, whereby the UE is protected against the core network computing cloud. It functions as a gateway to LaaS dedicated to physical position data.

All LSU components / elements (communication processor, downlink signal processor, uplink signal processor, and navigation processor) are implemented in software.

The following is a description of further system deployment options, including LSU placement.
1. 1. The LSU may be deployed inside the core network and / or the operator's IP service network.
2. LSUs can be deployed on servers in cloud computer-based centralized RAN (C-RAN) baseband processing edge facilities, for example on evolving 4.5G MEC (Mobile Edge Computing) servers, as hosted applications. Can be integrated. Note: RAN is a radio access network.
3. 3. LSUs are hosted within the core network computing cloud and / or the operator's service network cloud.
4. LSU can be a fully hosted and managed cloud service that connects to the operator's network infrastructure and / or IP service network via a secure remote internet connection.

Embodiments 3 and 4 support Location-as-a-Service (LaaS) data delivery, whereby the UE acts as a gateway to the core network computing cloud and LaaS dedicated to protected physical location data. .. However, option 3 is dedicated to 5G networks with evolved packet cores (EPCs) (also known as network cores) hosted within the computing cloud. At the same time, option 4 is more appropriate for currently deployed wireless network options as well as 5G deployments, and is therefore an embodiment. It should be understood that both the above overview and the following detailed description are exemplary and purposeful and are intended to provide a further description of the claimed embodiment.

FIG. 39 shows an embodiment of a system with a fourth option of LSU deployment based on the UE positioning architecture in E-UTRAN (3GPP TS 36.305 version 14.3.0 release 14). In this embodiment, OMA (Open Mobile Alliance) Secure User Plane Location (SUPL) architecture version 2.1 (OMA-AD-SUPL-V2_1-20120529-C) may be used. Unlike conventional E-UTRAN positioning using the LTE control plane, SUPL performs E-UTRAN positioning on the LTE user plane. The SUPL solution uses existing standards, such as LTE interfaces and protocols that support UE positioning, to transfer support and positioning data on user plane bearers such as IP to determine the location of SUPL-enabled terminals (SETs). You can do it. To accomplish this task, the SUPL solution extends existing LTE interfaces and protocols, such as the LPP protocol. Note: SETs can communicate with logical entities within the device, namely the SUPL Location Platform (SLP). SLP is responsible for location service management and position fixing. SLP includes SLC function and SPC function. The SUPL Positioning Center (SPC) entity within the SUPL network is responsible for all the messages and procedures required for location calculation and delivery of assistive data. The SUPL location center (SLC) coordinates the behavior of the SUPL in the network and interacts with the SET on the user plane bearer.

FIG. 40 shows a block diagram of an embodiment of a system architecture that is the basis of positioning. This figure shows a) one or more LTE devices 4001 (also known as UE), b) E-UTRAN eNodeB 4002, c) network core elements (mobile management entity (MME) 4005, advanced serving mobile location center (E-SMLC). ) 4009, Serving Gateway (SGW) 4003, and Packet Gateway (PGW) 4004, d) Network Core IP Service Network 4006, e) PoLTE LSU4007, and d) Standalone LMU or LMU integrated with DAS system 4010. ing. FIG. 40 includes an IP service network entity 4008 capable of managing / supporting location service (LCS) tasks. Note: Unit 4008 may also include other entities. In addition, in FIG. 40, LTE and SUPL interfaces and user planes are identified.

The present embodiment utilizes a SUPL position platform (SLP) solution to determine device (UE) position. However, there are some important architectural differences between the SUPL and PoLTE system architectures that require enhancement and / or modification of the SUPL solution and the introduction of new elements / features. The differences between these architectures are described below.

First, the system shown in FIG. 40 does not include SLP. Instead, the SLP function is performed by the LSU4007.

Second, the SUPL architecture places the SUPL location platform (SLP) with the core of the network as a stand-alone entity, or combines some elements of the location platform with the network core components, eg, SUPL on the E-SMLC4009. Integrate the location center (SPC) or connect to the SPC via a dedicated interface (see 3GPP TS 36.305 version 14.3.0 Release 14, section B.2 SUPL 2.0 and LTE Architecture). However, to support LaaS, LSUs are hosted in the cloud outside the core and / or core IP service network of the network. Therefore, in this architecture, the positioning elements do not have to be integrated with the core and / or service network components. Instead, the information is first integrated into the E-SMLC and this information via a user plane with a SUPL Llp interface (via an extended protocol of the interface) or via a dedicated interface and / or protocol. May be carried to a relay (see FIG. 40) entity that acts as a relay for passing the to the LSU.

Third, for downlink positioning, SUPL obtains support data from the SET, i.e., actions that take place entirely within the LPP / LPPe session. However, in some downlink positioning cases, the information from the eNodeB and UE needs to be merged for positioning operations. SUPL essentially runs on the user plane and does not support terminating operations in eNodeB. Therefore, operations that require support data supplied by various eNodeBs need to be performed on LPPa in combination with control plane procedures (3GPP TS 36.305 version 14.3.0 Release 14, section B.4). Procedures combining C-plane and U-plane operations). The LTE Release 14 solution integrates the SPC (SUPL Positioning Center) into the E-SMLC or connects the SPC to the E-SMLC via a dedicated interface.

However, this solution is limited to the collection / transfer of assistive data and does not support, for example, PoLTE uplink AoA / DoA and U-TDOA positioning operations that require the transfer of digital samples of reference signals. The solution also places several positioning functions within the core of the network, and according to the above architectural differences, this solution has all positioning functions in the core of the network and / or core IP services. PoLTE system architecture (Fig. 40) belonging to a single entity (LSU) outside the network cannot be supported.

Fourth, the PoLTE operation is from the digital sample of the baseband I / Q sample in the time domain or the baseband I / Q sample in the frequency domain, or from the resource element (RE) or OFDM symbol of the OFDM symbol that carries the reference signal. Requires collection / extraction and transfer to the LSU of the RE reference signal in the UE and / or LMU. This is a unique requirement that is not supported by either the LTE positioning standard, ie the UE positioning architecture within E-UTRAN, or the OMA SUPL architecture.

This approach has several advantages, the main advantages of which are listed below (other advantages are described throughout this disclosure).
1. 1. In the UE, the heavy calculation load required for positioning (calculation of RSTD) is reduced, so that the power consumption of the UE is significantly improved. This is very important for modems targeting IoT (Internet of Things) applications. At the same time, this approach allows the positioning engine to run continuously in the background, enabling ubiquitous high-precision positioning that provides up-to-date location information with low latency and without adversely affecting UE power consumption. Become.
2. The complexity of the LMU (Position Management Unit) is dramatically reduced, allowing seamless integration of the LMU and eNodeB. In its current form, the LMU is a complex standalone device that is fully specialized in receiving and processing uplink reference signals and calculating RTOA (relative arrival time) values (also known as measurements). be. Therefore, in its current form, LMU is not easily integrated into eNodeB. On the other hand, collecting / extracting a digital sample of a baseband reference signal and transferring it to LSU is a less complex task (labor) that exhibits only a light computational load.
3. 3. The LSU may be located within or outside the network and / or IP service network and hosted within the computing cloud. Therefore, it enables LaaS for the latest and future network architectures / environments.
4. A digital sample of the baseband I / Q sample of the reference signal can be performed via the control plane and / or the user plane, allowing uplink positioning on the user plane. This cannot be achieved with the SUPL solution.
5. The ability to process both uplink and downlink reference signals in a single entity LSU, for example, improves the reliability and positioning accuracy of locating systems, uplink / downlink or downlink / uplink. It is possible to advance features that were not possible until now, such as UE positioning that combines link / uplink.
6. The ability to handle multiple networks combines Bluetooth, WLAN, LTE, and more.

With reference to FIG. 40, the SET and LMU functions have been modified and the underlying protocol (not shown in FIG. 40) that delivers messages / information via the LTE / SUPL interface (FIG. 40) adapts to system requirements. Is extended to. Also, a new relay entity (see FIG. 40) will be introduced. It will be integrated into E-SMLC, which passes a sample of the uplink baseband reference signal, support data, and a digital sample of other information to the LSU.

As mentioned above, the LMU function may be implemented as a stand-alone LMU unit or integrated with other elements of the wireless network infrastructure, such as an LMU integrated with eNodeB, an LMU integrated with a DAS system, etc. May be carried out as. However, in all variants, a digital sample of the relevant UE uplink baseband reference signal is collected, preprocessed, transferred to the LSU along with support data and other information for further processing.

In addition, SLP and its key components, SLC and SPC, will also be modified and integrated into LSU. For example, changes include using signal processing and positioning calculation algorithms / techniques for uplink and / or downlink positioning, and support for receiving / acquiring uplink / downlink digital samples of baseband reference signals. Additions, expansion of auxiliary distribution functions to adapt to uplink location identification, etc. can be mentioned. In addition, Lup and Llp message support and underlying protocols have been extended to allow transfer / transfer of digital samples of baseband reference signals from the relay entity to the LSU.

An example of a SET modification would be to add the ability to acquire, preprocess, and transmit a digital sample of a downlink baseband reference signal by the SUPL Positioning Calculator (SPCF). Modifications to SPCF may also include preprocessing these samples prior to transfer to LSU. SUPL is an extended LPP (LTE Positioning Protocol) used to deliver information, including assistive information, between UEs and network core elements via the Uu interface and eNodeB, and to SLPs via Loop. Is. However, in one embodiment, the extended LPP protocol (LPPe) is further extended to accommodate digital sample delivery. The SET function, on the other hand, can be simplified by acquiring digital samples and sending them to LSU, and limiting these samples to preprocessing.

In essence, for any of the above LMU functions, the present embodiment uses an alternative LMU function. That is, it uses only the ability to take a digital sample of the uplink baseband reference signal via the relay entity, preprocess it, and send it to the PoLTE LSU. In addition, the eNodeB function can be extended to take a digital sample of the uplink baseband reference signal, preprocess it, and send it directly to the PoLTE LSU or via a relay entity.

Further, the LMU does not need to obtain the support information from the eNodeB and the E-SMLC because the support information can be collected by the relay entity, for example, the E-SMLC distributes the support information to the relay entity. The LMU uses the underlying SLM-AP protocol to communicate with the E-SMLC and relay entities via the SLm interface. The latter is extended to support digital sample transfer and LMU / relay communication.

This embodiment introduces a new entity (relay). The purpose is the current LTE / SUPL architecture (3GPP TS 36.305 version 14.3.0 Release 14, section B.4 Procedures combining C-plane and) described in the discussion on architectural differences (third point). It is to deal with the limitation of U-plane architectures). The relay entity passes the digital sample from the LMU to the server. The relay entity also enables communication between the server and the E-SMLC and allows the support data delivered via LPPa (LTE Positioning Protocol Annex) to be transferred to the server. LPPa allows eNodeB to exchange location information, including support data, with E-SMLC for UE positioning. At the time of the server's request, the assistive data may already be available in the E-SMLC, or the E-SMLC may obtain this data from the appropriate eNodeBs. Note: Support data may be for uplink AoA / DoA, U-TDOA, downlink OTDOA, and E-CID positioning. The relay entity communicates with the server via an LlP interface or a dedicated interface. In the case of an LlP interface, its message support and underlying protocol will be extended to allow the transfer / transfer of digital samples and / or support data of baseband reference signals.

The embodiment of the IP service network entity 4008 depends on the MNO. MNOs are mobile operators, also known as wireless service providers, wireless operators, mobile operators, and the like. Therefore, the server and unit 4008 command / control communication interface may be dedicated to the MNO. In this embodiment, it is assumed that all communication with the server is on the Internet and this connection is secure. Unit 4008 also channels the exchange of positioning information between the server and system elements. There are several ways to channel this information exchange, for example using protocol tunneling techniques.

In this embodiment, a user plane is used for transfer and communication of positioning data in the system. However, the introduction of relay entities makes it possible to implement another system that uses a control plane for the transfer and communication of positioning data within the system. As described above, the conventional E-UTRAN positioning uses the LTE control plane for the transfer and communication of the positioning data. These observations, such as RSTD, RTOA, AoA, etc., as well as data and support information are transmitted between the UE and / or LMU and E-SMLC via the underlying LPP, LPPa, and Slm-AP protocols. NS. The E-SMLC is responsible for determining the position measurement (Fig. 39). Similarly, for one embodiment, the LPP / LPPa and Slm-AP protocols transfer / pass digital samples of baseband reference signals and / or PoLTE dedicated support data to E-SMLC, and ultimately to relay elements. Extends to support. Communication / data exchange of the relay entity with the server is via the IP service network entity 4008 (FIG. 40). The interface and protocol may be dedicated or may be one of the LTE / SUPL interfaces that use the underlying protocol that has been modified / extended.

Moreover, this alternative system embodiment reduces complexity and, for example, simplifies the SET function or eliminates the entire SET element. Also, the SLC and SPC functionality of the SLP integrated into the server (in one embodiment) is significantly simplified and even eliminated altogether.

Yet another embodiment is shown in FIG. This embodiment uses only a small portion of the functions defined by the SUPL architecture and may include only the stand-alone LMU function and its associated E-SMLC, or in fact the LMU function in the eNB. The specific operating modes of this embodiment are listed below.
1. 1. UE clients and LSUs exchange data packets in their own format over the IP protocol.
a. The packet content may include measurement data, auxiliary information, or control commands.
b. The data packet is a Wi-Fi (4011) and / or using the LTE user plane, or, for example, 3GPP LTE-WLAN Aggregation (LWA) or 3GPP LTE WLAN Radio Level Integration with IPsec Tunnel (LWIP) technology (FIG. 41). It may be transferred via any alternative form of secure data bearer, such as a Wi-Fi + ePDG (4012) combination.
i. Note: The ePDG entity 4012 is responsible for the interaction between LTE EPC (Evolved Packet Core) and non-3GPP networks that require secure access, such as Wi-Fi, LTE Metro, and femtocell access networks.
c. Data packets may be exchanged directly between the UE and the LSU, or via an Internet of Things (IoT) platform such as Amazon Web Services (AWS) IoT, Google Cloud, or AT & T M2x.
i. Optionally, the Internet of Things (IoT) platform Internet of Things may be part of the IP Services Network Entity 4008.
2. E-SMLC and LSU exchange data packets in their own format over the IP protocol.
a. The packet content may include measurement data, auxiliary information, or control commands.
b. Data packets may be exchanged directly between the E-SMLC and the LSU, or via an Internet of Things (IoT) platform such as Amazon Web Services (AWS) IoT, Google Cloud, or AT & T M2x. (Fig. 41).
3. 3. LMUs (and / or LMUs within the eNB) and LSUs exchange data packets in their own format via the IP protocol.
a. Data packets may be exchanged directly between LMUs and LSUs, or via Internet of Things (IoT) platforms such as Amazon Web Services (AWS) IoT, Google Cloud, or AT & T M2x.
b. Data packets may also be exchanged between LMUs and LSUs via E-SMLC (FIG. 41).

In one embodiment, the wireless device of the exemplary LTE network may interface with one or more communication networks and / or one or more dedicated locating systems (networks). These networks (systems) may use downlink positioning, uplink positioning, or both.

Downlink positioning includes wireless devices that receive signals from one or more networks / systems that are used to locate the device. Currently, downlink positioning requires a wireless device to detect and process these signals, so that the processing is used in the positioning of the wireless device performed by the network components (E-SMLC). , Pseudo range, pseudo Doppler, etc. GPS / GNSS, TOA, TDOA, etc. Timing, AOA, arrival phase, etc. Includes calculating one or more of the observables such as directions. Alternatively, the wireless device may determine its position by performing both of the above calculations.

Uplink positioning includes elements of one or more dedicated networks that receive signals from one or more wireless devices that are used to locate the device. Similar to downlinks, current uplink positioning also requires network elements (eg, LMUs) to detect, process, and calculate these signals, while location calculations are another network component. Performed by (E-SMLC). Also, in some cases, the network element may also calculate the location of the wireless device.

Wireless devices and / or network elements may also receive auxiliary / assistive information messages that support location confirmation. However, in some embodiments, these messages and signals may be used to locate the device. For example, the GNSS message includes a distance measurement code (for timing calculation) and navigation data (auxiliary information).

In some examples, the radio device positioning signal is used specifically for device positioning, i.e., a specialized use. However, in other cases, these signals are dual purpose. For example, pilot signals present in many communication network transmissions may also be used to locate the device.

In this embodiment (unlike current embodiments), neither wireless devices nor specific network elements calculate signal observations. Instead, the wireless device and / or certain network elements collect and preprocess one or more snapshots of the signal used to position the wireless device, and the snapshots are GPS / GNSS pseudorange / It is transmitted to the LSU that determines the observable of the signal including the pseudo Doppler and executes the position confirmation of the wireless device. The computational load of collecting and preprocessing one or more snapshots is at least an order of magnitude lower than the computational power and resources currently required. Therefore, in this embodiment, the wireless device and / or certain network elements are freed from heavy computational loads and require less computational resources.

When communicating with the LSU (Positioning Server), the snapshot data consumes a wider bandwidth than the current embodiment. Subsequently, some of the energy savings achieved in this embodiment will be offset by the additional power consumed in transmitting the snapshot data. However, according to our power estimation calculations, this disclosure still results in significant power savings.

At the same time, the advantages of this embodiment outweigh the greater communication overhead of snapshots. For example, the wider bandwidth of a snapshot is very small, eg, less than 1% of the LTE uplink bandwidth (in all categories). Also, the larger snapshot communication overhead in relation to the communication of a particular network element with the LSU has no relevance (impact).

The advantages of this embodiment are listed below.
Wireless devices: longer battery life, better energy efficiency, lower complexity, cost, and size.
Specific network elements:
1) A stand-alone element that is fully specialized in receiving, detecting, processing, and calculating observables of signals used to locate devices can dramatically reduce complexity, reduce power consumption, and so on. Allows seamless integration with other network elements.
2) Other network components facilitate network component functions to include collecting and preprocessing snapshots of signals used for positioning wireless devices (snapshots are sent to LSU). Expanded.

For example, the LMU (Position Management / Measurement Unit) of a cellular network, in its current form, cannot be easily integrated with another network component, the eNodeB, without affecting the hardware and software of the eNodeB. , A complex stand-alone component that calculates signal observables. On the other hand, collecting and pre-processing snapshots and sending them to the LSU is a less complex task that puts only a small additional computational load on the eNodeB.

Another example involves the calculation of observables of one or more signals used to locate wireless devices, resulting in heavy computational and computational resource loads, APs, WLANs that may be subject to processing of these signals. It is a component. In addition, the computational constraints of APs (legacy) that are already installed prevent the deployment of state-of-the-art location algorithms that have heavy computational loads. At the same time, the present embodiment does not impose a heavy load on the computing resources of the AP because the collection and preprocessing of snapshots and the transmission of these to the LSU give the WLAN AP a very small computational load.
Other advantages are:
1) Deploy advanced algorithms with high computational bandwidth that provide better location accuracy and reliability without overwhelming the resources of existing network infrastructure components.
2) Significantly reduce (minimize) efforts to improve logistic (there are no restrictions on HW / SW legacy).
3) Deploy state-of-the-art machine learning to further improve the reliability of position confirmation and the accuracy of position measurement.
4) Continuously running wireless devices within the LSU, i.e. in the background, without increasing power consumption and without overwhelming the computational resources of the wireless device / specific network component.

In this embodiment, snapshots of the signals used to locate the device may be taken in digital form, i.e., digital samples. The digital sample is from the baseband signal, can be in the time domain or frequency domain, and can be represented by an I / Q sample, resource element (RE), channel state information (CSI), and the like. Digital samples are also antenna-by-antenna. Note: RE and CSI are complex coefficients for each OFDM subcarrier. The I / Q value represents the in-phase (I) component and the orthogonal (Q) component of the signal.

In one embodiment, a wireless device element and / or a network element is a detector (also known as a logical entity) configured to detect and extract a digital sample of a signal used to locate the device. It may be included and may be configured to collect and store a large number of digital samples for each antenna and for each signal identifier (ID). In addition, the detector may be configured to collect / store metadata such as each frame start and other auxiliary and / or support information.

In one embodiment, the detector is also configured to preprocess the collected (stored) digital stamps, eg, to reduce the data size prior to transmission to the LSU, eg, to reduce the communication bandwidth. You can. This reduction may include extracting digital samples that represent only the signals used to locate the device, i.e. excluding digital samples that have a payload.

In one embodiment, the detector may be configured to collect and retain a portion of a digital sample of data within a GPS / GNSS navigation message frame, i.e. a small portion of the message. In this approach, most of the information transmitted in navigation messages is available in LSU (GPS / GNSS assisted data), which includes reference time, reference position, satellite ephemeris, clock correction, ionospheric model, and earth. Orientation parameters, GPS time offset, acquisition support, ephemeris, UTC model, etc. can be mentioned.

In one embodiment, the detector may be configured to obtain a further reduction in data size by compressing the digital sample to a smaller number of bits. Compression of digital samples can be achieved by running a correlation engine within the detector by the following methods.

Emitter detection: The reference signal has high autocorrelation and low cross-correlation characteristics to support emitter (source) detection (discrimination) during simultaneous transmission from multiple emitters. Note: The reference signal can be discriminated based on the emitter's ID / parameters and / or the configuration of the reference signal. The OFDM symbol containing the reference signal is captured by the receiver. Any number of OFDM symbols, including reference signals, may be captured, processed, and limited only by available memory. Therefore, the input to the emitter detection engine is a complex two-dimensional array. Input data elements include superposition of reference signals from multiple emitters. The emitter detection engine searches the input data for a known reference signal generated by each emitter in a given located database / list. This search is performed at the antenna of the emitter by cross-correlating the input data reference signal with a known reference signal, i.e., an ideal replica of the reference signal, such as modulation. Therefore, the emitter detection function includes the generation of an ideal replica that produces an ideal replica of the reference signal associated with the emitter's ID / parameter and / or reference signal configuration. Like the input data, the ideal replica is in the form of a two-dimensional array containing as many REs as the positioning opportunity data. It is also indexed by the same frequency-sample index and symbol index.

Cross-correlation: The emitter detection engine performs cross-correlation in the frequency domain by multiplying each received OFDM symbol by the conjugate of the ideal OFDM reference signal. This is the frequency domain corresponding to the time domain matched filter. When the cross-correlation between the received reference symbol and the ideal reference symbol is applied, any coding scheme used to generate the reference signal is removed, leaving residual phase information for each subcarrier. The result of this operation is a phase coherent two-dimensional array. The phase coherent two-dimensional array is then summed and integrated. Any number of OFDM symbols may be integrated using this method. The magnitude and phase of the complex numbers in the output vector correspond to the presence or absence of a reference signal transmitted by the emitter and TOA in the receiver.

Detection: The detection process examines the magnitude and phase of the cross-correlation output and the reference signal transmitted by the emitter and captured by the receiver has sufficient characteristics to accurately estimate the TOA of the received signal. Determine if.

Compression: If the signal properties are sufficient, the cross-correlation output is considered valid and the non-zero portion of the complex vector is sent to the TOA estimation secondary processing block. In this method, the TOA estimation block may be located remote from the emitter detection engine.

In the case of a downlink position using CRS data received from a 2xMIMO emitter, there are 40 CRS OFDM symbols in one frame of LTE data. For an LTE bandwidth of 10 MHz, there are 600 subcarriers contained within each OFDM symbol. When the receiver is used for a 16-bit analog-digital receiver, each complex subcarrier has a full bit depth of 32 bits. In the data consisting of CRS data corresponding to one frame, the total size of the bits of the data set is 40 × 600 × 32 bits, that is, 768,000 bits.

However, using our coherent compression scheme, the size of the resulting data used for TOA estimation is reduced to a size obtained by multiplying the non-zero portion of the cross-correlation output by the number of detected emitters. The size of the data is reduced to 1 x 200 x 32 bits per detected emitter, or 6400 bits.

In the case of the uplink position using the SRS data received at the base station using the 10 subframe configuration, there are 8 CRS OFDM symbols in the LTE data of 1 frame. For an LTE bandwidth of 10 MHz, there are 600 subcarriers contained within each OFDM symbol. When the receiver is used for a 16-bit analog-digital receiver, each complex subcarrier has a full bit depth of 32 bits. In the data consisting of SRS data corresponding to one frame, the total size of the bits of the data set is 8 × 600 × 32 bits, that is, 1536000 bits.

Assuming a full bandwidth SRS signal, the size of the data in this case is reduced to 1 x 288 x 32 bits per detected emitter, or 9216 bits.

The coherent compression process has reduced the amount of data required for the number of symbols used in the cross-correlation process.

In addition, there are many compression algorithms that can further reduce the size of the data. Some are well known, such as A-law and u-law Compounding Algorithms. Some lesser-known algorithms are also used in radar technology, while others target fiber optic data transfer. Thus, the detector may be configured to compress the digital sample size, for example, from 32 bits to 16 bits or from 32 bits to 8 bits, resulting in a 2 or 4 times data size reduction.

In one embodiment, the detector performs matching filtering of the signal used to locate the device, calculation of the eigenvalues of the Singular Value Decomposition (SVD) principle of a matrix formed from one or more of the digital samples of the signal, etc. , May be configured to perform additional preprocessing (to further reduce the data size). This additional pre-processing is a trade-off for reduced communication bandwidth on the computational load / resource vs. location confirmation server.

For example, matching filtering can be combined with carrier frequency offset (CFO) processing per network node ID, followed by a large number of samples being integrated (in time) to further reduce the data size of the digital samples. In one embodiment, the data associated with the detection of the signal used to determine the position is larger than the backhaul data used to transmit the compressed signal. This is especially true for IOT deployments. For example, a Cat-M modem that typically uses a bandwidth of 1.4 MHz over a downlink to communicate with an LTE network refers to an LTE signal bandwidth of up to 10 MHz, such as a CRS signal that can be used for positioning. be able to. This is possible because the Cat-M modem is typically deployed within the 10MHz channel (in-band) used for normal LTE communication. The compressed information of the downlink signal is then used using the Cat-M uplink channel, or Wi-Fi, Bluetooth, ZigBee, other IEEE802.15 radio technologies, or currently existing or will be developed. It may be returned to the LSU using other low bandwidth communication technologies. The same type of embodiment can be used for NB-IOT, whereby more signals can be captured by the modem. As an example, the Cat-M downlink signal may be compressed and then sent back to the LSU via the NB-IOT data channel. An equivalent approach may be used in reverse order for the eNB, whereby the uplink signal can be captured, compressed, and then passed to the LSU using the lower bandwidth communication protocol as described above. .. This may allow the position solution to use more signals for position calculations. This can also lead to better position results for the following reasons: That is, it is well known that 1) position accuracy is inversely proportional to bandwidth, and having more bandwidth results in better position accuracy. 2) Having more bandwidth reduces the likelihood that the signal will be suppressed by interference and also allows for better multipath mitigation, especially considering the super-resolution techniques described herein. Become. 3) More signals can be used for integration.

In one embodiment, the exemplary positioning LTE network, auxiliary / support information includes:
a. Serving cell information: [physCellId, cellGlobalId, CellGlobalIdEUTRA-AndUTRA, earfcn-DL, systemFrameNumber, slot number, UTC time stamp, rsrp-Result, rsrq-Result];
b. ue-RxTxTimeDiff, DownlinkPathLoss, Bandwidth (in Physical Resource Block (PRB));
c. Adjacent cell information (0 to 32 adjacent cells): [physCellId, cellGlobalId, CellGlobalIdEUTRA-AndUTRA, earthcn-DL, systemFrameNumber, rsrp-Result, rsrq-Result];
d. UE information: [UE ID, UE category, mobility, Mobility History Report].

In one embodiment, the exemplary positioning LTE network, auxiliary / support information includes:
a. Site name:
b. Technology (ie 4G, 5G), active (ie wireless), indoor / outdoor;
c. Global cell ID, PCI value, frequency, IsGPS synchronized, DL Tx configuration (ie, number of Tx ports, maxTx power, DL bandwidth);
d. Tower structure type (ie rooftop, monopole, side of building, etc.), cable length and loss; antenna type (ie omni, directivity), latitude, longitude, antenna altitude AGL (ie above the surface), Tower base altitude MSL (ie, average interface), geoid;
e. For each antenna used to capture signals: antenna azimuth, elevation, mechanical downtilt / electrical downtilt, gain, H-beam width, V-beam width;
f. Cell bandwidth, TA, list of adjacent cells, PRS configuration, optional;
g. Configuration of eNodeB sector antenna array.

In one embodiment, the sounding reference symbol transmitted by the UE may be captured and reported with auxiliary information about the configuration assigned to the UE during SRS transmission. This information may include some or all of the following elements of the UL Configuration IE as per 3GPP TS 36.355.
-Serving cell PCI
-Call Timing Advance-Cell UL-bandwidth
-UL-CycloPrefixLength
-Cell srs-BandwidthConfig
-UE srs-Bandwidth
-UE srs-AntennaPort
-Srs-HoppingBandwise
-Srs-cyclicShift
-Srs-ConfigIndex
-TransmissionComb
− FreqDomainPosition
-GrowpHoppingEnabled
-DeltaSS
-SFN start time

In one embodiment, the capture of the sounding reference symbol data may be complemented or replaced by the capture of the uplink demodulation reference symbol transmitted by the UE.

In one embodiment, the demodulation reference symbol shall be complemented by the following auxiliary / support information.
a. Push transmission start time: Frame and subframe number b. Uplink permission information:
(I) Frequency hopping flag (ii) Resource block allocation and hopping resource allocation c. Circular shift of DM RS and OCC indexes d. PUSCH configuration:
e. n-SB
f. Hopping mode g. pushingHoppingOffset
h. UL-ReferenceSignalsPUSH:
(I) growPhoppingEnabled,
(Ii) groupAssignmentPUSCH
(Iii) sexhoppingEnabled
(Iv) cyclicShift

In one embodiment, two or more demodulation reference symbol blocks and associated ancillary information can be captured and reported.

In one embodiment, exemplary snapshot metadata information includes a description and type of communication network / location system, location, signal classification (downlink or uplink), signal description, type, structure and parameters, date. / Time stamp, snapshot data size, data creation source (wireless device / device ID, network / system element (network / system element ID, etc.), data compression information, availability of auxiliary / support information, wireless subframe start Examples include the time offset of the first reported digital sample.

In this embodiment, the snapshot data may be categorized (by logical entity) along with metadata and associated auxiliary / assistive information and is referred to as a positioning opportunity. Positioning opportunities may occur on a regular basis.

According to one embodiment, the logical entity of the wireless device and / or network element organizes positioning opportunity data (snapshot data, metadata, auxiliary / support information), exchanges it with the LSU, commands and other It may include a communication processor configured to receive information. Note that signaling can be in accordance with industry standard interfaces / protocols such as the OMA SUPL protocol and / or 3GPP LPP, Internet Protocol security (IPsec), and the like. Dedicated interfaces and / or protocols may also be used.

In this embodiment, the LSU may include a positioning engine configured to determine the location / location of the wireless device from one or more related positioning opportunities. The output of this engine may also include position-related metadata (metrics) such as confidence radius values from the Fourth Report and Order (FCC), GDOP, and radio E911 position accuracy metrics.

In one embodiment, the LSU is configured to interact with one or more wireless devices, one or more communication networks / location systems (network / system components and / or services) and positioning engines. It may include a management processor. The location management processor a) manages all messages between one or more wireless devices, network / system (network / system elements, network services and all messages between the LSU, b) one. It is configured to manage the delivery of positioning opportunities (snapshot metadata and / or support / auxiliary data) from the above logical entities to the positioning engine, and c) manage security.

In one embodiment, the location management processor receives a positioning opportunity (from one or more logical entities), calculates a target location, and passes this information to a positioning engine for tracking, or navigation. The positioning engine then returns the target's location and tracking information (to the location management processor) along with location / tracking-related metadata that may be provided, for example, to network services, wireless devices, and so on.

The precision positioning method uses a two-step position process, where the first step is one or more observations, namely TOA, TDOA, TOF, AOA / DOA, received signal phase, and their result metrics. Accompanied by related calculations (SNR, standard deviation, reliability, etc.). During the second step, the observations and their metrics are used to determine the (target) location / navigation of the wireless device.

At the same time, there are other positioning methods that use a one-step positioning process, i.e., do not require the aforementioned observations (observables), such as RF fingerprinting, direct positioning and the like. However, the slightly lower complexity of the one-step process is the result of sacrificing reduced accuracy, ambiguity in location, and other performance-constraining phenomena.

According to the present disclosure, the accuracy of the results of two-step process observables (TOA, TDOA, TOF, etc.) can be independently improved by multipath mitigation using the advanced spectral estimation (super-resolution) algorithm described above. .. Similarly, AOA / DOA's unique improvement / adaptation is the time difference of the ranging signal at the time of reception at each antenna, that is, the phase difference between the above super-resolution estimate of TDOA and the ranging signal collected by each antenna. Combine with the AOA / DOA techniques to be compared.

According to the present disclosure, improvements in the accuracy and robustness of the second step in the two-step position process (positioning / tracking) described above apply heuristic ad hoc positioning / tracking techniques, as well as common positioning algorithms, to communication network signal structures. And includes adaptation within the network environment.

According to the present disclosure, the two-step process also adapts to the one-step position process. Wireless device elements and / or network elements are configured to detect and extract probability statistics (data) for device location, and per antenna and per signal identifier (ID). This information may be configured to be collected and stored. In addition, the detector may be configured to collect / store metadata and / or other auxiliary / assistive information. The detector can classify these stochastic statistics along with its metadata and associated auxiliary / assistive information to generate stochastic positioning opportunities. The communication processor included in the logical entity is then configured to organize and exchange these positioning opportunity data with the LSU.

In one embodiment, according to a two-step process, the positioning engine includes a signal processing unit and a data processing unit, the signal processing unit receives positioning opportunity information from the position management processor, and the position management processor is the data processing unit. Receives target location and tracking information, along with location / tracking-related metadata / metrics from. Each unit uses auxiliary / support information and snapshot metadata included in the positioning opportunity.

In one embodiment, the signal processing unit estimates observations of downlink and / or uplink signals from one or more communication networks and / or one or more dedicated locating systems (networks) and their metrics. It may be configured to do so.

In one embodiment, the signal processing unit may be configured to estimate the carrier frequency offset (CFO) using one or more specific algorithms and / or techniques, whereby the signal processing unit of the LSU may be configured. It may be possible to track one or more mobile wireless devices and reduce clock frequency inconsistencies between nodes in one or more networks and wireless devices. The CFO estimate is used to correct the snapshot digital sample from which the observable is calculated.

In one embodiment, the data processing unit may be configured to perform location and tracking using the output of the signal processing unit, i.e., observer / metric. In addition to target position measurements, the data processing unit produces metric data containing a covariance matrix of multiple position confirmation results obtained from numerous combinations of observers and their standard deviations.

In one embodiment, the data processing unit uses observables and their metrics obtained from one or more communication networks and / or one or more dedicated locating systems (networks) for downlink and uplink positioning. / May be configured to perform tracking. Data processing units should also use a combination of downlinks and / or uplink observables / metrics from one or more communication networks and / or one or more dedicated location systems, ie, hybrid positioning / tracking. It may be configured.

In one embodiment, the data processing unit uses multilateration (also known as hyperbolic positioning), trilateration, triangulation, and excellent DOA / AOA / E-CID positioning methods to obtain position measurements and their metrics. You can. Multilateration is further enhanced by forming a location-specific cost function using reliability metrics to increase the probability of identifying the minimum cost function (eg, position measurement).

In one embodiment, the signaling unit and the data processing unit perform a probabilistic locating algorithm to determine the positioning of the wireless device, i.e., one or more wireless devices and / or one or more network elements. It may be configured to execute a one-step positioning process using the stochastic positioning information from the logical entity.

In wireless network deployments, it may not be possible to use multilateration / three-range multilateration, which requires at least three reference points (in the case of 2D position identification) to obtain the position of the wireless device (UE). For example, in some environments, only two high power cells or APs are deployed to deliver large amounts of RF signals over a wide area. This raises issues with these methods, which depend on at least three detectable reference points. To alleviate this problem, the present disclosure uses heuristic ad hoc positioning / tracking techniques and adapts common positioning algorithms to and within the network environment of communication network signals.

In one embodiment, the multilateration / trilateration method divides the set of all detectable reference points (nodes of the network) into three or more subsets, determines the target position of each subset, and performs machine learning. It is further improved by performing target position confirmation by applying it to a plurality of position estimates for which a position matching algorithm such as an algorithm has been obtained.

In cellular networks, the excellent DOA / AOA positioning of the present disclosure is based on the E-CID method described above. This is a single tower based uplink position process with two observables (round trip path delay (RTT) calculated by the serving cell and horizontal (azimuth) beamwidth of the serving sector). This method suffers from lack of accuracy because the sector antennas in current deployments have large (60 degrees or 120 degrees) horizontal beamwidths. As mentioned above, the improvements / adaptations unique to this disclosure utilize existing sector antenna diversity (using MIMO sector antennas for AOA / DOA estimation). This effectively reduces the AOA angular error to less than 1 degree. However, in order to obtain the position measurement in 2D, it is necessary to grasp the distance from the UE to the serving cell in addition to the AOA / DOA estimation. This distance may be derived from round trip time (RTT) / time advance (TA) estimates based on measurements performed by the serving cell. Also, when estimating RTT / TA, the serving cell receiver does not mitigate radio wave propagation phenomena, such as multipath.

As a result, RTT measurements may lack the required accuracy. One way to address accuracy issues is to use the algorithms of the present disclosure to calculate the above distance (instead of RTT) from the downlink TOA observable between the serving cell and the wireless device (UE). Is. The drawback of this solution is that this downlink TOA is a one-way measurement and requires accurate synchronization between the serving cell and the wireless device, i.e. the position accuracy is affected by this synchronization error. .. On the other hand, the wireless device is locked to the serving cell, reducing the clock frequency mismatch due to the CFO estimation and correction disclosed above, thus reducing this error and not compromising the accuracy gain achieved by the algorithms of the present disclosure. ..

In cellular networks, timing advance (TA) is used to compensate for propagation delay as the signal travels between the UE (wireless device) and the sector antenna tower of the serving cell. The base station of the serving cell allocates TA to the UE based on the measured distance of the UE (see FIG. 37).

In LTE, timing-advanced type 1 measurements (see FIG. 38) correspond to round trip time (RTT), or round trip propagation delay of the signal. Timing advance propagation delays may be from DLOS / direct paths or reflection paths and include propagation delays from cell tower cables and base stations / UE electronics. Further, the UE adjusts the transmission timing with four times the Ts accuracy (Ts is the LTE system timing equal to 32.55 ns).

Type 1 is defined as the sum of the transmission / reception timing difference in the eNB and the transmission / reception timing difference in the UE as follows.
TA = TADV = (eNB Rx-Tx time difference) + (UE Rx-Tx time difference).
Therefore, the distance d to the base station is estimated using the following equation.
d = c ^* (TADV / 2) (in the formula, c is the speed of light) or d = c ^* (RTT / 2) (in the formula, c is the speed of light).

TA (RTT) is available from the serving cell and represents a UE range estimate independent of the serving sector. However, TA cannot be obtained from the UE (wireless device). Instead, the UE provides a transmit / receive timing difference, i.e., access to the UE Rx-Tx. From the above, Rx-Tx = RTT-eNB Rx-Tx of the UE. However, from FIGS. 37 and 38, when the UE TA is adjusted, the Rx-Tx time difference of the eNB of the serving cell becomes the same for all UEs. As a result, the measurement of Rx-Tx in the UE still corresponds to the RTT.

The RTT is equal to the TOF / TOA estimate biased according to the cable length of the cell tower antenna and the electronics of the base station.

In one embodiment, the propagation delay of the antenna cable length can be estimated from the height of the tower, and the propagation delay of the base station electronics can be estimated from the statistical data collected from the various towers. ..

There are many cases in which positioning with a combination of downlinks and uplinks can be used to overcome the shortcomings of a single downlink or uplink location. For example, in an environment where signals from only two nodes can be used, estimating the combination of downlink TOA / TDOA and uplink AOA / DOA (from the serving BS) can improve location accuracy / reliability in cellular networks. can. This is because the additional uplink AOA / DOA constraint reduces the ambiguity of downlink UE (wireless device) 2D positioning resulting from the number of reference points (cells) less than three.

In another example, additional constraints from the AOA / DOA estimate may result in further improvements to the heuristic ad hoc positioning / tracking techniques described above.

Furthermore, even if observations from three or more nodes are available, additional AOA / DOA constraints include node synchronization error, inadequate GDOP, sector antenna coordinate error (height, etc.), Helps reduce the inherent errors of many networks that affect multilateration / true-range multilateration, such as uncompensated cable delay errors (calibration errors). This AOA / DOA estimation does not include these errors because the MIMO sector antenna used for the AOA / DOA estimation of the serving cell is time and phase coherent. Note: AOA / DOA LOB is equal to the angle between the baseline of the sensor (antenna) and the incident RF energy from the radio device. If the angle between the baseline and, for example true north, is known, the true quadrant azimuth line (LOB) and / or AOA can be determined. Baseline angle errors can also affect AOA / DOA accuracy, but less than the overall effect from the above errors.

At the same time, RTT observables collected from serving cells or wireless devices can also provide additional TOF / TOA constraints that help mitigate a number of network-specific errors described above. Therefore, downlink and uplink hybrid positioning may also include additional constraints (from the AOA / DOA and RTT TOF / TOA observers) for reducing positioning errors.

Hybrid positioning can also be extended across two or more networks. For example, positioning of indoor wireless devices can be achieved based on observables from WLANs and cellular networks. The WLAN location confirmation RSSI system is very common, but its accuracy is low. Also, the accuracy of the multilateration method is severely affected because the access point (AP) clocks are only loosely synchronized with each other. Lack of precise synchronization between the AP and the device also reduces TOA / TOF accuracy. For accurate positioning, the AOA / DOA method is used with an observer determined by a WLAN AP equipped with MIMO antennas (typically using three or more individual antennas).

The integrated framework / platform and proprietary improvements of the AOA / DOA algorithms of the present disclosure allow LSU signal processing and data processing units to calculate accurate LOBs from one or more APs. As a result, this location-based hybrid approach, which is based on a combination of WLAN and cellular network, will have higher accuracy than single network-based positioning. This is because the combination of location confirmation provides better spatial diversity that negates the RF propagation phenomenon and also reduces the inherent individual network defects.

In this embodiment, the data processing unit may use multilateration (also known as hyperbolic positioning) to locate the wireless device. Hyperbolic navigation is based on timing differences, ie TDOA observables, and does not refer to a common clock. The data processing unit may also be configured to use the observable metrics described above to reduce ambiguity in multilateration position measurements and to apply position matching algorithms such as machine learning algorithms.

The multilateration method involves solving a large number of hyperbolic (TDOA, also known as RSTD / TDOA in cellular networks) equations, and a number of different algorithms / approaches can be used to find the correct solution. Note: The RTOA (Relative Arrival Time) observable is a form of TDOA in uplink positioning within a cellular network.

In the context of overdetermined systems, where there are more equations than unknowns, i.e., 4 or more independent observers are available for 2D positioning, this solution is an initial estimation of the position of the radio device or " Includes an iterative approach starting with "guess". Estimates can then be improved at each iteration by determining the position solution of the local linear least squares method.

One drawback of this approach is that the initial position estimation needs to be closer to the final position solution to ensure the convergence and / or absence of local values that can result in significant position errors. On the other hand, the overdetermined situation reduces the possibility of ambiguous and / or unrelated solutions that can occur when only the minimum required observables can be used, for example, when only three can be used for 2D positioning. do.

From the above, the iterative approach needs to have an accurate initial estimate of the UE position. This estimation can be improved by using one of the disclosed hybrid methods described above or a combination of single network based downlinks and uplinks.

Comprehensive downlink / uplink signal processing capabilities from various networks / systems in a single entity, LSU of the integrated framework / platform of the present disclosure improves the reliability and positioning accuracy of locating systems. Enables advanced hybrid / fusion positioning, such as uplink-downlink or downlink-uplink radio positioning combinations, which was not possible before.

Returning to the consideration of the embodiment of the LSU, the position management processor of the LSU is a communication processor configured for signaling and information exchange with a wireless device, and a communication network / location identification system (network) such as a network / system element. ) And may be further included. Signaling is the OMA SUPL protocol and / or 3GPP LPP / LPPa, or a combination of LPP, LPPa, and SUPL, Internet Protocol Security (IPsec), and other protocols used or can be used to communicate with the network, such as , Cellular LCS-AP protocol and / or IPsec / IKEv2 or proxy mobile IPv6 protocol. Note: The latter protocol is used by the Evolved Packet Data Gateway (ePDG) element, which is responsible for the secure interaction between cellular and other networks, such as WiFi, LTE Metro, and femtocells. In addition, dedicated interfaces and / or protocols may be used.

In this embodiment, the communication processor is also configured to collect word-wide GPS / GNSS satellite data from the Word-Wide Reference Network (WWRN) station. Note: World Wide Reference Network is a ground surveillance station with so-called support data.

The assistive data is used by LSU's positioning engine (signal processing unit and data processing unit) for positioning the wireless device.

The tracking algorithm or tracker provides the ability to predict future positions of multiple moving objects based on the history and velocity of individual object positions reported by the sensor system.

In this embodiment, the position measurement of the wireless device (target) and its metric for each positioning opportunity are used by a tracker algorithm that continuously estimates the target position and target speed, such as the position / velocity reliability metric. Therefore, over time (ie, a large number of positioning opportunities), the tracking algorithm reduces the standard deviation of position / velocity estimates and improves positioning accuracy.

In one embodiment, the data processing unit is configured to use a Kalman filter, a particle filter, and an improved α-β filter to correct / smooth position and / or velocity estimates, eg, to perform tracking. It's okay. The α-β filter uses the position / velocity reliability metric to adjust the α-β value.

Furthermore, in another embodiment, the data processing unit may also be configured to generate user interface (UI) information from tracker output (position measurements and their metrics) and other information contained in positioning opportunity data. ..

In this embodiment, all LSU components / elements (signal units, data processing units, location management processors, communication processors, etc.) may be implemented in software. Some options for LSU deployment (server software execution) are listed below.
1. 1. The LSU may be deployed within the core and / or operator's IP service network of the network.
2. LSUs can be deployed on cellular network servers in cloud computing-based centralized RAN (C-RAN) baseband processing edge facilities, such as evolving 4.5G MEC (Mobile Edge Computing) servers. It may be integrated as a hosted application. Note: RAN is a radio access network.
3. 3. LSUs are hosted within the core network computing cloud and / or the operator's service network cloud.
4. The LSU can be deployed outside the core and / or operator's IP service network of the network and can be connected to one or more networks.
5. LSU is a fully hosted and managed cloud service that may be deployed outside the network's core and / or operator's IP service network, to one or more networks via a secure remote internet connection. Be connected.
6. The LSU has some LSU components / elements (such as signal data acquisition and data processing units) instantiated and integrated with the virtual eNodeB instance, or deployed in close proximity to the virtual eNodeB instance (eg, same cloud). It may be deployed within a cloud RAN architecture (on a processing unit or within a processing unit that has a direct interface to a cloud processing unit that supports eNodeB instances).
7. The LSU may be deployed as a stand-alone entity or within any private wireless network (such as a wireless LAN, Citizens Broadband Radio Service, and LAA) that integrates with several elements of the wireless network.
8. The LSU may be incorporated into the E-SMLC or a variant thereof, or may replace some of the functions of the E-SMLC to be involved in the positioning of the LSU.

Fifth options support a) current 4G and next 5G cellular wireless network deployments, b) non-cellular network / system deployments, and c) Location-as-a-Service (LaaS) location confirmation data delivery. , Thereby, the wireless device acts as a gateway to support protected physical location data (by LSU). Therefore, this option is an embodiment of the present disclosure.

Further, the present embodiment is a heterogeneous multi-network and / or a plurality of types of access node environment. In a multi-network environment, one or more communication networks and / or dedicated location systems, as completely separate entities, each serving a particular application, eg, cellular, WLAN, etc., and / or GPS / GNSS, It exists as one or more dedicated positioning systems such as the Terrestrial Beacon system. In the case of multiple types of access nodes, it is called a HetNet environment. In addition, an environment that combines one or more networks with HetNet is also supported.

Note: HetNet indicates the use of multiple types of access nodes in a wireless network. Wide area networks provide macrocells, small cells (micro / pico / femto) and / or DAS to provide radio coverage in environments with wide radio coverage zones ranging from open outdoor environments to office buildings, homes, and underground areas. Can be used.

In addition, there is a special case called HetNet (Heterogeneous Wireless Network (HWN)). HetNet may also consist of elements / components with different capabilities with respect to operating system, hardware, protocol, etc., and HWN is a wireless network consisting of devices that use different underlying wireless access technologies (RATs). ..

The current multi-network and multiple types of access node environments are shown in FIG. This includes GPS / GNSS and Terrestrial Beacon dedicated locating systems, in addition to LTE and Wi-Fi wireless communication networks.

Various node types include Macrocell 4202 with Base Station 4203, Metrocell 4204, Small Cell: Outdoor / Canvas-4204, Indoor-4214; WLAN AP4218, Active DAS (Indoor / Campus) -4230 and Passive DAS (Indoor Only) ) -4224; Terrestrial Beacon 4208 and LMUs (Position Measurement / Management Units): Indoors-4220, Outdoors-4210, and Integrated LMUs (not shown) that may be present at Macrocell base station 4203. Note that LMU is not integrated with DAS Base Station (BS) 4225.

Also shown in FIG. 42 is a wireless device 4260, also known as a UE, which may be a handset, wireless IoT sensor, or tag. The wireless device 4260 also receives downlink transmissions from the GPS / GNSS satellite 4250.

The current LTE EPC (Evolved Packet Core) is shown in FIG. 43A. FIG. 43A includes the core IP service network 4306 of the network and the entity 4308 of the IP service network capable of managing / supporting location services (LCS) tasks. It may also include other entities. The IP service network 4306 and the IP service network entity 4308 are not part of the EPC.

According to FIG. 43A, the node is connected to an LTE EPC (evolved packet core) via a backhaul network 4240 (FIG. 42). The backhaul of FIG. 42 includes a small cell to the EPC, a metro cell, and one or more gateway / aggregation points 4242 that support Wi-Fi connectivity. For example, the WLAN controller 4216 is connected to an EPC component, or PGW 4304, via an ePDG (Evolved Packet Data Gateway). Backhaul networks may also support connections between nodes and LSUs. The WWRN4252, which is part of the GPS / GNSS system, communicates with the EPC components.

As shown in FIG. 43A, the macrocell backhaul connection terminates at the EPC MME4305 and SGW4303 components. Data from the metrocell, small cell and / or small cell controller 4212 passes through the gateway / aggregation point 4242 and then reaches the MME4305 / SGW4303. At the same time, the data from the WLAN controller 4216 passes through the backhaul gateway 4242 and then terminates at PGW 4304. The LMU indoor 4220 and / or outdoor 4210 nodes are communicating with the E-SMLC4309 component and the WWRN4252 that provides the E-SMLC with the necessary satellite / system support information, which is then wirelessly transmitted by the E-SMLC. This information may be used when flowing to the device or determining the position measurement of the target in AGPS / AGNSS mode.

The specific operation modes of this embodiment are listed below. Unlike current location-specific architectures, neither the E-SMLC4309 nor the UE 4260 of the present disclosure perform wireless device position calculations in this embodiment. Similarly, the LMU network elements of the present disclosure do not perform wireless device location calculations. Also, neither the UE nor the LMU have calculated observables and their metrics. Further, in the current architecture, auxiliary / support information is collected and distributed by E-SMLC, whereas in the architecture of this embodiment, this information is collected and distributed by the LSU of the present disclosure.

For command and control / status message exchange, industry standard interfaces / protocols / procedures, such as OMA SUPL or 3GPP (via E-SMLC), or alternative protocols such as MQTT may be followed. Dedicated interfaces and / or protocols / procedures may also be used.

1. 1. Downlink location confirmation:

Downlink positioning opportunity data packets, including GPS / GNSS, are transmitted from the wireless device to the LSU (by the logical entity of the disclosure residing on the device). These data packets may be forwarded on the LTE user plane by extending the LPP (LTE Positioning Protocol) to convey the positioning opportunity information of the present disclosure.

Currently, LPPs are used for data exchange between UEs and networking core elements, and in the case of OMA Secure User Plane Location Architecture (SUPL), extended LPPs (LPPes) are UEs and SUPL location platforms (SLPs). ) Is used for data exchange. This communication method (using LPPe) can also be used by LSU as needed to provide assistive / auxiliary information to the wireless devices of the present disclosure.

Alternatively, the data packet may be forwarded via any other form of secure data bearer, such as Wi-Fi connected to the EPC data (user) plane via a backhaul gateway. Other Wi-Fi options include using 3GPP LTE-WLAN Aggregation (LWA) or 3GPP LTE WLAN Radio Level Integration with IPsec Tunnel (LWIP) technology.

Another option is to exchange data packets between the UE and LSU via an Internet of Things (IoT) platform such as Amazon Web Services (AWS) IoT, Google Cloud, or AT & T M2x.

Furthermore, another way for the wireless device (UE) of the present disclosure to pass data to the LSU (exchange data with the LSU) is to use a protocol such as MQTT. MQTT is a publish / subscribe message protocol designed for lightweight communication between machines (M2M). It is used by Amazon Web Services, Azure, and many other cloud-based solutions. Further options include Advanced Message Queuing Protocol (AMQP), Streaming Text Oriented Messaging Protocol (STOMP), IETF Constrained Application Protocol (STOMP), IETF Constrained Application Application and Protocol, XMPPP, DS.

One advantage of such a publish / subscribe mechanism is that the location server (LSU) does not need to know the UE identifier of the cellular network, such as the UE IMSI or IMEI, or its IP address. Instead, all communications use identifiers defined and managed by location services. Communication can occur equally over 3G, 4G, 5G, and / or Wi-Fi and the like. In fact, communication does not have to use a cellular network. Communication may use any type of connection that provides an internet connection.

In addition, dedicated interfaces and / or protocols may be used.

2. Uplink position confirmation:

In its current form, the LMU is entirely special in receiving and processing an uplink reference signal to calculate a form of TDOA, an RTOA (relative arrival time) observable (also known as an uplink measurement). It is a complex, stand-alone device. LTE standard release 11 and beyond specify the integration of LMU and eNodeB, but in the current form, LMU is not easily integrated into eNodeB and / or other devices.

On the other hand, the element of the network that executes the LMU logical entity of the present disclosure is a low-complexity task (labor) that presents a very small computational load. This allows for all types of eNodeBs, i.e. macrocells, small cells, metrocells, and easy paths to integrate LMU logical entities with other devices such as WLAN APs, active DAS headend units. In addition, the complexity, cost, and power consumption of the stand-alone (indoor / outdoor) LMUs of the present disclosure are significantly reduced.

Therefore, in this embodiment, the uplink positioning opportunity data packet is transmitted to the LSU from a network element integrated with the LMU of the present disclosure (by the resident logical entity of the LMU). At the same time, the uplink positioning opportunity data packet is also transmitted from the stand-alone (indoor / outdoor) LMU of the present disclosure.

In the current uplink architecture, the LMU uses the SLm interface Application Protocol (SLmAP) to exchange data packets with the E-SMLC via the SLm interface (see Figure 43A). Therefore, the data packet may be forwarded directly to the LSU using the SLM interface and the SLmAP may be extended to convey positioning opportunity information. This communication method can also be used by the LSU as needed to provide assistive / auxiliary information to the LMUs of the present disclosure.

Alternatively, the positioning opportunity data packet may be forwarded through another form of secure data bearer over an internet connection, eg, a connection that provides internet protocol security (IPsec). Further, the uplink positioning opportunity data may be transmitted to the LSU via the wireless device logical entity of the present disclosure. Communication may take place over 3G, 4G, 5G, and / or Wi-Fi and the like. The drawback of this approach is the additional overhead (load) on the available bandwidth.

Another option is to exchange data packets directly between the LMUs and LSUs of the present disclosure via Amazon Web Services (AWS) IoT, Google Cloud, AT & T M2x, Azure and many other cloud-based solutions. Is.

Yet another option is similar to the downlink approach, where the LMUs of the present disclosure use protocols such as MQTT with Amazon Web Services, Azure, and many other cloud-based solutions. To exchange data with LSU.

Using the communication link established between the LMUs of the present disclosure and the LSUs, the uplink position confirmation uses the LMUs of the present disclosure as macrocell eNodeB 4203, active DAS headend unit 4231, small cell 4214/4206, metro cell 4204. , And can be achieved by integrating with WLAN AP4218.

3. 3. LSU connection of the present disclosure:

43B and 43C show the connections of the LSU embodiments. In this embodiment, all communication with the LSU is via the Internet, and this connection is assumed to be secure, eg, via IPsec. Other options may also be used (see downlink and uplink descriptions above).

In one embodiment, all positioning functions belong to a single entity (LSU) outside the core (EPC) and / or core IP service network 4306 of the network. Therefore, signal processing, location identification, tracking, and navigation are all performed within the LSU, as described in some embodiments. Further, in the architecture of this embodiment, the support / auxiliary information is also collected and distributed by the LSU, including data from the WWRN received via the backhaul network. Note: WWRN is part of the GPS / GNSS system.

The LSU also exchanges positioning opportunity data packets and other information with one or more stand-alone LMUs according to the present disclosure and / or one or more integrated LMUs according to the present disclosure. At the same time, LSU exchanges positioning opportunity data packets and other information with one or more wireless devices of the present disclosure.

Here, the exchange of data packets and other information can be performed via the IP protocol and in its own form. Alternatively, data packets and other information can be exchanged via platforms such as Amazon Web Services (AWS), Google Cloud, AT & TM2x, Azure, and many other cloud-based solutions. In addition, protocols such as MQTT can be used for data packets and other information exchanges. Other options include Advanced Message Queuing Protocol (AMQP), Streaming Text Oriented Messaging Protocol (STOMP), and the like. Dedicated interfaces and / or protocols may also be used.

In one embodiment (FIG. 43C), all and other information exchange of the above LSU data packets may be performed via the IP service network entity 4308.

In another embodiment (FIG. 43B), the communication link between one or more LMUs and LSUs and other sources does not use the IP service network entity 4308. Therefore, the backhaul network may also support a direct connection between one or more network nodes and the LSUs of the present disclosure.

In this embodiment, unit 4308 channels data packets and other information exchanges between the LSUs of the present disclosure and the mobile devices and / or one or more network elements of the present disclosure, such as the LMUs. There are several ways to channel this information exchange, for example using protocol tunneling techniques.

The embodiment of IP service network entity 4308 depends on the MNO. MNOs are mobile operators, also known as wireless service providers, wireless operators, mobile operators, and the like. Therefore, the LSU and unit 4308 command / control communication interface of the present disclosure may be dedicated to the MNO.

4. E-SMLC:

In this embodiment, the LSU of the present disclosure essentially inherits the functionality of the E-SMLC4309, but commands and control / status message exchanges that can be performed via the E-SMLC in accordance with 3GPP standard interfaces / protocols / procedures. There may be exceptions for. At the same time, these tasks can be performed based on the OMA SUPL approach or by using alternatives such as the MQTT protocol. In addition, dedicated interfaces and / or protocols / procedures may be used for this purpose. Therefore, it is not necessary to use E-SMLC for exchanging commands and control / status messages.

Another possible exception is when support / auxiliary information from one or more eNodeBs is required to locate the wireless device. Note: E-SMLC uses LTE Positioning Protocol A (LPPa) to obtain this information. Again, the LSU may be configured to obtain this information by other means.

However, it may be necessary to use some of the E-SMLC services during the phased implementation. It is a relay of FIGS. 43B, 43C, and 43D configured to collect and store the above-mentioned support / auxiliary information, which is the logical entity of the present disclosure, in E-SMLC, which is an element of the network. It can be achieved by including entity 4310. The relay entity may also be configured to deliver this information to the LSU. Also, the relay logic entity may be configured to support the exchange of the above commands and control / status messages executed via E-SMLC in accordance with the 3GPP standard.

The relay logical entity and the LSU are with the description of Part 3 above, either through the IP Service Network Entity 4308, which is consistent with the description of the LSU connection of the present disclosure in Part 3 above, or directly without the IP Service Network Entity 4308. You can communicate by the same means / methods. Dedicated interfaces and protocols may also be used.

Based on the above explanation, it is as follows.
1. 1. In one embodiment, the LSU location management processor may be configured to obtain assistive / auxiliary information from one or more eNodeBs with or without interaction with E-SMLC.
2. In one embodiment, the LSU location management processor may be configured to support the exchange of commands and control / status messages as per the 3GPP standard, with or without interaction with E-SMLC.

Further, in one embodiment, the LSU location management processor of the present disclosure exchanges and assists positioning opportunity data packets and other information with one or more LMUs of the present disclosure and one or more wireless devices of the present disclosure. / All auxiliary information may be exchanged via E-SMLC to support the exchange of command and control / status messages with E-SMLC.

Such an embodiment is illustrated in FIG. 43D and uses the relay entity described above configured to collect all of the positioning opportunity data packets and other information such as assistive / auxiliary information transmitted to the LSU. The relay entity will also be responsible for the communication between the LSU and E-SMLC of the present disclosure.

According to this embodiment, the communication protocol currently used (by E-SMLC) may be extended to support the positioning opportunity formats (data) of the present disclosure, such as the LPP, SLmAP, and LPPa protocols.

According to this embodiment, all communication between the relay logic entity and the LSU of the present disclosure takes place over the Internet and this connection is secure. Other options may be used (see description above). Dedicated interfaces and protocols may also be used.

According to this embodiment, the relay entities and LSUs of the present disclosure are directly through or without the IP service network entity 4308, which is consistent with the description of the LSU connection of the present disclosure in Part 3 above. , Can be communicated by the same means / methods as described in Part 3 above.

In another embodiment, the LSU positioning engine (signal processing and data processing unit) function of the present disclosure is integrated with E-SMLC. According to this embodiment, the LSU function is performed in the EPC component E-SMLC.

An embodiment of the unified framework / platform is shown in FIG. This is a multi-network and multi-type access node environment, including LTE and Wi-Fi wireless communication networks, as well as GPS / GNSS and Terrestrial Beacon dedicated locating systems.

Unlike the current environment shown in FIG. 42, the present embodiment includes macrocell eNodeB 4403, active DAS (4430) headend unit 4431, small cells: indoor 4414 (small cell controller 4412) and outdoor 4406, metro cell 4404 and WLAN AP4418. (WLAN controller 4416), Stand-alone LMU of the present disclosure: Supports the LMU of the present disclosure integrated into the indoor 4120 and outdoor 4110. Note that the LMUs in this disclosure are not integrated with DAS Base Station (BS) 4425.

Also shown in FIG. 44 is a wireless device 4460, also known as a UE, which may be a handset, wireless IoT sensor, or tag. The IOT sensor can be embedded in an object that requires tracking. The wireless device 4460 also receives downlink transmissions from the GPS / GNSS satellite 4450.

Communication between these elements and the LSUs of the present disclosure is carried out via the backhaul 4440, outside the EPC, i.e. without the involvement of any of the EPC elements. These communication links are shown in blue.

Further, the communication (data) between the Word-Wide Reference Network (WWRN4452) and the LSU of the present disclosure is performed outside the EPC via the backhaul 4440. This communication link is also shown in blue.

According to the above disclosure, the network-centric architecture supports LaaS data delivery for 5G and other networks when all signal processing and location estimation is done in the cloud, ie outside the UE and / or eNodeB. It is stated that it will be designed in. There are several options as to how this can be done. In the case of uplink location identification, the transmission of the associated UE reference signal can be collected / preprocessed by the eNodeB, transferred to the location identification server unit (LSU) for further processing, and determining the position of the UE. In the case of downlink (OTDOA), the task of collecting and preprocessing the downlink reference signal may be performed by the UE. The UE may then send the collected downlink data to the LSU. In the case of downlink (OTDOA), the UE may also use the control plane and / or the LTE user plane to handle communication with the LSU. Therefore, signaling may be in accordance with the OMA Secure User Plane Localization (SUPL) protocol and / or 3GPP, such as the LTE Positioning Protocol (LPP). For uplink AoA / DoA, the eNodeB may use LPPa and SLmAP (SLm application protocol) to process communication with the LSU. In addition, dedicated interfaces and / or protocols may be used.

According to the above, this network-centric architecture can enable advanced features that were previously impossible. For these functions, (a) downlink OTDOA is used to measure the distance between the serving cell / tower and the UE and the uplink AoA / DoA to determine the position of the UE, while the tracking algorithm / By using the technique, the influence of the OTDOA synchronization error on the uplink / downlink UE positioning can be reduced, and (b) when the UE position is determined by using the RTT method in addition to the uplink AoA / DoA. Tracking algorithms / techniques can be used to improve UE positioning, and (c) tracking algorithms / techniques can be used to estimate, correct / mitigate, synchronization errors in the downlink OTDOA positioning method.

According to the above, a method for locating a downlink (OTDOA) UE is also described when the navigation processor uses a multilateration technique / method (also known as hyperbolic navigation). This multilateration technique involves solving a large number of hyperbolic (RSTD / TDOA) equations. There are iterative and non-repetitive (closed forms) solutions. In one embodiment, a hybrid that divides the number of available (audible) reference points (base stations) into multiple sets of three RSTD / TDOA subsets and finds a closed form solution for each subset. The approach is described. The position matching algorithm can then be used to make the final decision on the position measurement. In a second embodiment, the position measurement may be improved by utilizing a combination of iterative and non-repetitive solutions from the same set of RSTD / TDOA values. In a third embodiment, the uplink AoA / DoA estimates and RTT may be used to determine initial position estimates for UE positions based on iterative and non-repetitive algorithms.

According to the above, the wireless network environment uses only two high power cells to send a large amount of RF signals over a wide area, so a multilateration method that requires at least three reference points (in the case of 2D positioning). It is stated that position measurements may be obtained without following. Therefore, there is described how the UE is located along the arc defined by the azimuth beamwidth of the serving sector and the RTT / 2 range when RTT is available. Also described are methods in which the UE position can be determined near the intersection of the hyperbola and the arc when AoA / DoA estimates are available. Both of these methods may be used. If neither RTT nor AoA / DoA estimates are available, the UE position is determined by scoring the intersections corresponding to each cell / tower (sector) on the selected hyperbola 3608 (see Figure 36). Will be done. Scoring is based on the angular difference between the direction the cell is facing and the direction to a point on the hyperbola, and the distance from each point to the corresponding cell / tower. Scores can be weighted according to the SNR of these corresponding cells / towers.

According to the above, the LSU may include a communication processor, eNodeB, and / or network elements that are configured for signaling and information exchange with the UE. Signaling conforms to the OMA SUPL protocol and / or 3GPP LPP / LPPa, or a combination of LPP, LPPa, and SUPL, and other protocols used or may be used to communicate with the network, such as the LCS-AP protocol. You can do it. In addition, dedicated interfaces and / or protocols may be used.

According to the above, the LSU component may be an instruction stored in memory and configured to be executed on a processor of a 4.5 GMEC (Mobile Edge Computing) server located at the edge of a communication network. LSU components may be integrated as hosted apps on 4.5G MEC. In 5G deployments, LSU components may be hosted within the core network computing cloud. LSUs hosted within the core network computing cloud may support LaaS data delivery, whereby the UE acts as a gateway to the core network computing cloud and LaaS dedicated to protected physical location data.

According to the above, the LSU may include a downlink signal processor as well as an uplink signal processor and a navigation processor.

Thus, although different embodiments of the system and method have been described, it will be apparent to those skilled in the art that certain advantages of the described methods and devices have been achieved. Specifically, one of ordinary skill in the art will be able to build a system for tracking and locating objects using a combination of FPGA or ASIC and standard signal processing software / hardware at a very small additional cost. You will understand. Such systems are useful in a variety of applications, such as locating people in indoor or outdoor environments, harsh and unfavorable environments.

It should also be understood that various modifications, adaptations, and alternative embodiments thereof may be made within the scope and purpose of this disclosure.

Claims (27)

A method for locating wireless mobile devices within heterogeneous wireless communication environments, including cellular networks, wide local area networks, standalone private networks, satellite networks, and one or more of terrestrial wireless networks.
In the detector
Receiving a signal from one or more receiving channels, including a parameter of the location confirmation signal of the wireless mobile device, wherein the parameter of the position confirmation signal is a previously known signal within the signal. To describe, and
Receiving each frame start of the signal and receiving first metadata including one or more of the first support data and the first auxiliary information.
To detect the previously known signal from each receive channel in the one or more receive channels based on the parameters of the position confirmation signal.
Buffering multiple samples in digital format extracted from the previously known signal,
To reduce the data size of the plurality of samples by compressing the plurality of samples in a digital format,
Sending the compressed data, the parameters of the location confirmation signal, and the metadata from the detector to a physically remote location server.
In the location identification server
Receiving the compressed data, the parameters of the position confirmation signal, and the metadata from the detector.
Receiving the parameters of the heterogeneous wireless communication environment
Receiving a second metadata including one or more of the second support data and the second auxiliary information from the heterogeneous wireless communication environment, and
The heterogeneous radio communication is based on one or more of the compressed data, the parameters of the heterogeneous radio communication environment, the second metadata, and the second support data and the second auxiliary information. Calculating additional parameters for the environment and
Based on the compressed data, the parameters of the location confirmation signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata, and the second metadata of the wireless mobile device. To calculate the position and
Performing tracking and navigation of the wireless mobile device and
A method comprising reporting the results of said tracking and said navigation to said heterogeneous wireless communication environment. The method of claim 1, wherein the first auxiliary information includes one or more sensory information and sensor data. The location identification server
Interacting with the wireless mobile device and the heterogeneous wireless communication environment,
Interacting with the positioning engine,
Managing messages between the wireless mobile device, the heterogeneous wireless communication environment, and the location-specific server.
A location management processor configured to perform one or more of managing the delivery of data to the wireless mobile device and the heterogeneous wireless communication environment, and providing security, claim. The method according to 1. In the positioning engine
Receiving the compressed data, the parameters of the location confirmation signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata, and the second metadata from the location management processor. When,
Using the data received from the position management processor, performing position estimation and position tracking calculation, and
Performing position estimation using one or more of the uplink observables and downlink observables derived from the data received from the position management processor.
Calculating position-related metrics that include a radius of confidence, geometric dilution of accuracy, and one or more of radio E911 position accuracy.
The method of claim 3, further comprising providing the position estimation and position tracking calculation, the position estimation, and a position related metric to the position management processor. The method of claim 4, wherein the positioning engine continuously provides up-to-date information on the position estimation and position tracking calculations, the position estimation, and the position related metrics provided to the position management processor. The method according to claim 3, wherein the positioning engine is a navigation processor. The positioning engine includes a signal processing unit and a data processing unit.
The signal processing unit receives the compressed data, the parameters of the position confirmation signal, the parameters of the heterogeneous wireless communication environment, the additional parameters, the first metadata, and the second metadata. To generate the output of the signal processing unit including the observer bubble,
Receiving position estimation and position tracking calculations, position estimation, and position-related metrics from the data processing unit in the position management processor.
Claims that the data processing unit uses the output of the signal processing unit to perform location determination and tracking of the wireless mobile device and generate metric metadata from the location identification and tracking. Item 3. The method according to item 3. The support information and / or the auxiliary information, including data from the World-Wide Reference Network (WWRN) station received via the backhaul network, is also collected and distributed by the LSU, where the WWRN The method of claim 3, which is part of the GPS system and / or the GNSS system. The location management processor
Signaling and exchanging data and information with one or more communication networks and / or wireless devices (UEs) of location systems.
Signaling and exchanging data and information with the one or more networks and / or locating systems, including elements of one or more networks and / or locating systems, the World. -Include the LCS and the Worldwide GPS / GNSS satellite data from the Wide Reference Network (WWRN) station, and use one or more networking protocols, interfaces, computing platforms, and / or dedicated protocols and interfaces. The method of claim 3, comprising a communication processor configured for. The signal processing unit
The location observable is to estimate the position observables and their metrics of the downlink signal and / or the uplink signal from one or more communication networks and / or one or more locating systems. Includes TOF (flying time), TOA (arrival time), TDOA (arrival time difference), AOA (arrival angle), and DOA (arrival direction), and is described in US Pat. No. 7,872,583. Improving the result accuracy of the observable using a multilateration processor that includes methods, techniques, and algorithms.
To perform said position observable calculations with mixed signal and sensory information by comprehensive signal processing methods, techniques, and techniques, including combinations of methods, techniques, and techniques. The method of claim 7, which may be configured. The LSU and / or the LSU component, including a signal unit, a data processing unit, a location management processor, and a communication processor,
The IP service network of the core and / or operator of the network,
A cellular network server for cloud computer-based C-RAN (Centralized Radio Access Network (RAN)) baseband processing edge facilities, such as the evolving 4.5G MEC (Mobile Edge Computing) server, and a hosted application. Can be integrated as a cellular network server,
Within the core network computing cloud and / or the operator's service network cloud (ie, hosted within the cloud),
Outside of said core and / or operator's IP service network of the network, connected to one or more networks.
A fully hosted and managed cloud service that is connected to one or more networks via a secure remote internet connection, outside the network's core and / or operator's IP service network.
A cloud in which some LSU components are integrated or juxtaposed with a virtual eNodeB instance and deployed within the same cloud processing unit or a processing unit that has a direct interface to the cloud processing unit that supports the eNodeB instance. Within the RAN architecture,
Within any private wireless network, such as a wireless LAN, Citizens Broadband Radio Service, and Selected Assisted Access (LAA), as a stand-alone entity or integrated with several elements of the wireless network.
3. The third aspect of the invention, which is deployed in one of the E-SMLCs, which is fully integrated or replaces some of the functions of the E-SMLCs in order to relate to the positioning of the LSUs. the method of. The LSU is
To work with current 4G and next 5G cellular wireless network deployments,
Working on non-cellular networks and / or systems,
Acting on the Location-as-a-Service (LaaS) location confirmation data delivery, the wireless device acts as a gateway to the protected physical location data (by the LSU).
Operating outside the UE and / or the eNodeB, all signal processing and location estimation is performed within the cloud, and outside the core and / or core IP service network of the network. 11. The method of claim 11, which is to operate and all of the signal processing, location, tracking, and navigation can be configured to be performed within the LSU. The LSU is
Operating in a heterogeneous multi-network, one or more communication networks and / or dedicated location systems exist as completely separate entities, cellular and WLAN and / or GPS / GNSS and / or Terrestrial Beacon, respectively. To fulfill the function of a specific purpose such as a dedicated position identification system,
To operate in multiple types of access node environments, such as the HetNet environment, HetNet is a term used for communication networks consisting of a combination of different cell types and different access technologies.
11. The method of claim 11, which may be configured to operate in an environment that combines one or more networks with the HetNet. The wireless device and / or network element may include a detector, which may include a detector.
Detecting and extracting digital samples of signals used to locate devices,
Collecting a large number of digital samples by antenna and by signal identifier (ID),
Collecting and storing metadata, including the start of each frame and auxiliary and / or support information,
Preprocessing a digital sample collected prior to being transmitted to the LSU to reduce the data size, which reduction is digital representing only the signal used to locate the device. It may include extracting a sample, as well as
It is configured to collect a portion of the data sample within the GPS / GNSS navigation message frame, which is a small part of the message.
The rest of the information transmitted in this navigation message is available in the LSU as the GPS assisted data and / or the GNSS assisted data.
The method according to claim 1, wherein the support data includes a reference time, a reference position, a satellite ephemeris, clock correction, an ionospheric model, an earth orientation parameter, a GNSS time offset, acquisition support, an almanac, a UTC model, and the like. The detector can either be integrated with or juxtaposed with the wireless device and / or network elements, the detector can be a sample of the signal from the receiver of the wireless device (UE). The method of claim 14, wherein the method of receiving and other relevant information. 14. The method of claim 14, wherein the elements of the network can be LMU and / or eNodeB, or a combination of both. The detector includes a communication processor, which processor
Organize sample data,
Signaling and exchanging data and information with said LSUs using one or more networking protocols, interfaces, computing platforms, and / or dedicated protocols and interfaces, and
14. The method of claim 14, which is configured to transmit sample data and metadata to the LSU to receive commands and support information. The communication processor is configured to return data and information to the LSU using the different cell types and / or one or more access technologies such as cellular, WLAN, Bluetooth, ZigBee, and IEEE802.15 radio technologies. The method according to claim 14. The data channel (of the UE) of the device is allocated within (internally) the bandwidth of the wider bandwidth channel, and the receiver of the device locates the device beyond the bandwidth of the allocated channel. The detector can access the signal used in the LSU to process the signal inside and outside the allocated channel bandwidth and transmit the obtained sample data and other relevant information to the LSU. The method of claim 14, which is configured. The detector
Configured to detect and extract said probabilistic statistics, eg, device (UE) location confirmation data, and to classify these probabilistic statistics along with their metadata and associated auxiliary and / or support information. The method according to claim 14. The detector
Using one or more compression algorithms, such as A-law and u-law companding algorithms,
Reducing each digital sample size to fewer bits,
By using the coherent compression scheme, the size of the obtained data sample used for observable estimation (transmitted to the LSU) is said to be the cross-correlation output of the signal captured by the receiver. The size is reduced by multiplying the non-zero part by the number of detected unique ID (identifier) signals.
After successfully detecting the signal used to locate the device by using a compression scheme, a further operation is matching filtering combined with carrier frequency offset (CFO) processing for each ID of the received signal. The method of claim 14, wherein the method of claim 14 is configured to include, followed by integration (in time) of a large number of samples of these received signals to achieve further data size reduction. The detector performs additional compression processing such as matching filtering of the signal used to locate the device, calculation of the singular value decomposition (SVD) principle eigenvalues of a matrix formed from one or more of the digital samples of the signal. 21. The method of claim 21, configured to perform and reduce the data size, where this additional processing is a trade-off between computational load and / or required resources and reduced communication bandwidth in the LSU. .. The detector and the LSU
Sample data, metadata, auxiliary information and / or support information, signaling, and / or using LTE interfaces and protocols such as OMA SUPL, 3GPP LPP, LPPa, or a combination of LPP, LPPa, SUPL, and / or LCS-AP. Or transfer communication content consisting of commands,
Transferring the communication content using a WLAN bearer such as the 3GPP LTE-WLAN Aggregation (LWA) or the 3GPP LTE WLAN Radio Integration with IPsec Tunnel (LWIP) technology.
Transferring the communication content via a computing platform such as Amazon Web Services (AWS), Google Cloud, AT & T M2x, using the Message Questing Telemetry Transport (MQTT) and / or alternative connection protocols.
Transferring said communication content via any alternative form of secure bearer, such as Internet Protocol Security (IPsec), IPsec with IKEv2 and Proxy Mobile IPv6, and
The method of claim 1, which is configured to transfer the communication content via a dedicated interface and / or protocol. The evolutionary packet data gateway (ePDG) is used for interaction between the LTE and non-3GPP networks that require secure access, such as WLAN, LTE metro and femtocell access networks. 23. The LSU is outside the network and the IP service of the network, the positioning of the device (UE) is determined outside of any of the network elements, and the E-SMLC using the relay unit is the E-SMLC.
Transferring sample data, metadata, auxiliary and / or assistive information, signaling, and / or commands between the detector and the LSU, and using or using the SUPL Llp interface and underlying protocol. 11. The method of claim 11, which is configured to go through an interface and / or protocol. 25. The method of claim 25, wherein the LMU uses the extended SLm-AP protocol to communicate with the E-SMLC and the relay entity via the SLm interface to accommodate the LMU and the relay communication and transfer. .. 25. The method of claim 25, wherein the relay unit has access to all location confirmation related information such as samples and support data.

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