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WO2017121168A1 - 簇式磁场定位的方法、装置和系统 - Google Patents

簇式磁场定位的方法、装置和系统 Download PDF

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Publication number
WO2017121168A1
WO2017121168A1 PCT/CN2016/103427 CN2016103427W WO2017121168A1 WO 2017121168 A1 WO2017121168 A1 WO 2017121168A1 CN 2016103427 W CN2016103427 W CN 2016103427W WO 2017121168 A1 WO2017121168 A1 WO 2017121168A1
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WIPO (PCT)
Prior art keywords
target
magnetic field
positioning
signal
time
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PCT/CN2016/103427
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English (en)
French (fr)
Inventor
王新珩
张聪聪
陈涛
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无锡知谷网络科技有限公司
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Priority to EP16884720.0A priority Critical patent/EP3404439A4/en
Publication of WO2017121168A1 publication Critical patent/WO2017121168A1/zh
Priority to US16/032,351 priority patent/US20180329022A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • G01S13/522—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves
    • G01S13/524—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves based upon the phase or frequency shift resulting from movement of objects, with reference to the transmitted signals, e.g. coherent MTi
    • G01S13/534—Discriminating between fixed and moving objects or between objects moving at different speeds using transmissions of interrupted pulse modulated waves based upon the phase or frequency shift resulting from movement of objects, with reference to the transmitted signals, e.g. coherent MTi based upon amplitude or phase shift resulting from movement of objects, with reference to the surrounding clutter echo signal, e.g. non coherent MTi, clutter referenced MTi, externally coherent MTi
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/292—Extracting wanted echo-signals
    • G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification

Definitions

  • the present invention relates to the technical field of indoor positioning, and in particular to a method, device and system for cluster magnetic field positioning.
  • the Chinese Patent Publication No. CN 102509059 A proposes a method of indoor positioning using RFID.
  • the method embeds an ultra-wideband signal transmission module based on the RFID technology.
  • the target exchanges information with the reader by transmitting an RF signal, and then the reader issues a positioning command. After the target receives the positioning command, it transmits an ultra-wideband signal.
  • the reader is connected to a plurality of receiving antennas, and after each antenna receives the ultra-wideband signal, the analog signal is used to simulate the signal from The antenna is transmitted, and the highest energy gathering point is the target position.
  • 201410201147.7 applies the fixed node and the magnetic field information (including strength and orientation) at the fixed node and the position information of the fixed node in the server. After receiving the magnetic field information (including intensity and orientation) and position information of the fixed node, the mobile node corrects its own position information by receiving the magnetic field information and the position information of the fixed node marked in the server. Here the magnetic field information of the fixed node is fixed.
  • an object of the present invention is to provide an indoor positioning method, apparatus and system with low complexity, small calculation amount, and high precision.
  • a method for cluster magnetic field positioning comprising: acquiring a wireless signal and a magnetic field signal; performing a first positioning of the target according to the received wireless signal; within a range of the first positioning The second positioning of the target is based on the magnetic field signal.
  • a cluster magnetic field positioning apparatus comprising: a receiver for receiving a wireless signal; a magnetic sensor for acquiring a magnetic field signal; and for, according to the acquired wireless signal strength, a positioning device that performs a first positioning of the target; and a predictive updating device for performing a second positioning of the target based on the range determined in the first positioning and the received magnetic field signal.
  • a cluster magnetic field positioning system comprising: the apparatus for indoor positioning described above; and a signal transmitter for transmitting a wireless signal.
  • the present invention can divide the space where the target to be located into a small space by using a wireless signal such as an RFID signal, an IRID signal, a WIFI signal, or a Bluetooth signal, thereby forming a cluster to perform the target.
  • a wireless signal such as an RFID signal, an IRID signal, a WIFI signal, or a Bluetooth signal
  • a rough positioning and then accurately locate the target within the range of the first positioning, reducing the area and data when using particle filter positioning, and also avoiding the problem of robot abduction, thereby reducing the amount of calculation and reducing The complexity of the calculation and the accuracy of the positioning.
  • FIG. 1 is a schematic flow chart of a method for cluster magnetic field positioning according to an embodiment of the present invention
  • FIG. 2 is a schematic flow chart of the second positioning of the target in FIG. 1;
  • FIG. 3 is a schematic flow chart of a cluster magnetic field positioning method according to another embodiment of the present invention.
  • FIG. 4(a) is a schematic diagram showing the distribution of an RF transmitter in an interior according to an embodiment of the present invention
  • Figure 4 (b) is a schematic view showing the distribution of the signal intensity of Tag 5 in Figure 4 (a);
  • Figure 5 (a) is a schematic diagram of simulation results of positioning of the target according to the RFID signal and the magnetic field signal;
  • Figure 5 (b) is a schematic diagram of the simulation results of the target positioning according to the magnetic field signal
  • Figure 6 (a) is a schematic diagram of the comparison of the target based on RFID, magnetic field signal positioning and only based on the positioning of the magnetic field signal;
  • Figure 6 (b) is a schematic diagram of the time required to locate the target according to RFID, magnetic field signal positioning and only based on the magnetic field signal;
  • FIG. 7 is a schematic diagram of functional modules of a cluster-type magnetic field positioning device according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a functional module of a cluster magnetic field positioning system according to an embodiment of the present invention.
  • FIG. 1 is a flow chart showing a method of cluster magnetic field positioning according to an embodiment of the present invention. As shown in Figure 1, the method includes:
  • S101 Acquire a wireless signal and a magnetic field signal.
  • the electromagnetic signal detecting unit carried by the target can detect the signal emitted by the RF/IR transmitter and the magnetic field (for example, geomagnetic) signal detector carried by the target itself (for example)
  • the PNI Sensor Corporation's MicroMag3 three-axis magnetometer can be used to measure the magnetic field in the room.
  • S102 Perform a first positioning on the target according to the wireless signal.
  • the first positioning is determined according to a proximity algorithm.
  • Nearest Neighbor Neearest Neighbor
  • the algorithm generally only provides relative position information of the target.
  • RF/IR emitters are placed in many places in the location area of the system, and the position coordinates of these emitters are known. Therefore, when the target moves to a vicinity of a certain transmitter, the receiver receives the radio frequency signal of the corresponding electronic tag, and the approximate position of the target can be known. If the target receives signals from several transmitters at the same time, the position of the target can be determined by comparing the intensity values of the received signals.
  • the above-mentioned proximity algorithm positioning is easy to implement, and the hardware requirements are not high. Therefore, in some occasions where the positioning accuracy is not high, the algorithm is very suitable.
  • the received signal strength is compared, and the signal range radiated by the signal transmitter with the highest signal strength is the coarse positioning area of the current target position.
  • S103 Perform a second positioning of the target according to the magnetic field signal within a range of the first positioning.
  • the above method for indoor positioning is particularly suitable for indoor global setting. Bit.
  • FIG. 2 is a schematic flow chart of the second positioning of the target in FIG. As shown in Figure 2, the process includes:
  • the second positioning is to predict a second position of the target according to the strength of the magnetic field signal.
  • the position of the prediction target can be implemented by a particle filter algorithm.
  • Bayesian estimation in particle filter algorithms generally consists of two phases, prediction and update.
  • the ultimate goal of the algorithm is to obtain the updated value, which is the posterior probability density of the target state. Therefore, the ultimate goal of particle filtering is to obtain the posterior probability density of the target state.
  • x t represents the X coordinate of the target at time t (can be pre-calibrated according to the map. Then, according to the characteristics of the target, the position of the target in the map is found. This method of calibration and searching is similar to the GPS method) and Position status,
  • y t represents the Y coordinate and the position state calculated by the observation function at the time t
  • Ft is the state transition equation function for state x t-1 at time t
  • h t is the observation function of state x t at time t
  • z t represents the value of the magnetic field strength of the target at time t
  • the state transition equation function indicates that the target current state X t is determined by its previous state X t-1 and noise.
  • the function is nonlinear and not fixed, and can be summarized by multiple measurements.
  • the observation function Y t is calculated based on the combination of the intensity distribution of the signal in the grid map and the signal detected by the target at the current position.
  • z 1:t-1 ) represents the posterior probability density distribution of the target at time t
  • x t-1) represents the prior probability density function distribution of the target at time t.
  • S202 Perform the predicted second position according to the strength of the wireless signal and the magnetic field signal. Update.
  • the state prediction value is updated by the Bayesian criterion, and the state update equation is:
  • x t represents the coordinate and position state of the moving target at time t (ie, the target coordinate point and the target orientation);
  • zt represents the value of the magnetic field strength of the target at time t;
  • y 0 ) represents the initial distribution function .
  • the posterior probability density of the target (ie, the current position of the target) can be calculated, and this iterative recursive relationship constitutes a Bayesian estimate.
  • Particle filtering is based on the large number theorem using Monte Carlo algorithm to achieve the integral operation in Bayesian estimation. The essence is that the posterior probability density of the target is approximated by a random discrete measure composed of the position of the particle and its different weights, and the discrete random measure is recursively updated according to the algorithm.
  • the particle filter algorithm is used to calculate the target posterior probability in Bayesian estimation.
  • ⁇ v t , t ⁇ N ⁇ represents measurement noise
  • ⁇ w t , t ⁇ N ⁇ represents observation noise
  • noise refers to an electrical signal that does not carry useful information.
  • the source of noise can be classified into radio noise, industrial noise, sky noise, and internal noise.
  • Internal noise is the main source of noise, also known as undulating noise, which refers to thermal noise and device noise inside the channel and cosmic noise from space. This noise is an irregular random process.
  • the thermal noise in the system channel is Gaussian white noise.
  • the noise source is an additive noise combining random noise and Gaussian white noise.
  • the added noise is random noise (S ⁇ U[S 1 , S 2 ]) and Gaussian white noise of the radio frequency system. Plus Gaussian white noise in the geomagnetic field system
  • the existing noise model (Gaussian white noise) can be used directly to simulate the experiment.
  • S203 Perform multiple predictions and updates on the position of the target according to the strength of the magnetic field signal.
  • This embodiment can be terminated by continuously predicting, updating, re-predicting, and re-updating through such training and learning after reaching a certain number of times. Can greatly improve the accuracy of target positioning.
  • the wireless signal may be a signal such as an RFID signal, an IRID signal, a WIFI signal, and a Bluetooth signal.
  • a signal such as an RFID signal, an IRID signal, a WIFI signal, and a Bluetooth signal.
  • the positioning process is divided into two stages.
  • the first stage determining that the mobile target is within the range of radiation of a certain wireless signal (eg, a signal of an RFID/IRID tag) based on the distribution of the wireless signal and the proximity algorithm. Since the signal radiation range of each RF is known and determined, the range of the target position can be narrowed down within the range of a single RF signal radiation.
  • a certain wireless signal eg, a signal of an RFID/IRID tag
  • the second stage in the range of the above single RF signal radiation, the particle filter algorithm based on the magnetic field fluctuation is used to determine a more accurate position of the moving target.
  • the complexity of the algorithm is simplified, the amount of calculation is reduced, and the positioning accuracy is improved.
  • FIG. 3 is a schematic flow chart of a cluster magnetic field positioning method according to another embodiment of the present invention.
  • the following is an example of positioning the robot of the stroller (ie, the above target).
  • the positioning method includes:
  • the initial particles are evenly distributed indoors, and the position of the particles is Particle weight is The number of particles Ns is 200.
  • the particles are some point in the algorithm, and each particle represents a possibility that the target position is within the current range of motion.
  • S302 The cart simultaneously receives the RSSI signal and the magnetic field strength emitted by the RF transmitter.
  • the cart first determines the approximate range of the indoors according to the RSSI signal strength emitted by the RF transmitter, that is, the cart is coarsely positioned.
  • the approximate range may be, for example, how many meters around a certain RF transmitting node is determined; or a circular area centered on the transmitting node or other related area; or may be some The radiation range of the RF signal, etc.
  • the particle filter is used to estimate the relatively accurate position of the robot of the trolley by referring to the geomagnetic field distribution within the radiation range of a certain RF signal.
  • Ns particles are randomly sampled in the region obtained by the above coarse positioning to obtain a "new" particle set.
  • the importance sampling method is adopted, that is, based on the Monte Carlo method, the objective function is approximated by random sampling.
  • the state of each particle is determined according to the coarse positioning of the particle filter in the previous step. Update to
  • the true distribution of the target ie the robot that holds the cart
  • Suggested distribution for the goal are different.
  • the proposed distribution is calculated empirically, so the closer the proposed distribution is to the true distribution, the better.
  • the general true distribution is obtained by accurate measurements.
  • the database that can be used in this embodiment can be composed of three grid maps of the same size of the grid map, respectively, with Three magnetic field magnitudes are indicated.
  • the magnitudes of these three magnetic fields are scalars, which are the magnitude, horizontal and vertical components of the geomagnetic field.
  • Traditional Hx, Hy, and Hz are not scalars. Because the moving direction of the target in practice is random, and Hx and Hy will change with the moving direction of the target, the mesh distribution of Hx and Hy will change, and the size of Hx and Hy will be affected by the direction of the sensor. , which in turn affects the accuracy of the subsequent positioning.
  • the three magnetic field quantities (whose values are scalars) can reduce errors and improve the accuracy of subsequent positioning.
  • the final goal is to estimate the position of the target by accumulating particles with weights.
  • S305 Determine whether the robot of the walking cart is walking.
  • Figures 4 to 6 depict a schematic representation of the positioning of the experimenter while walking in the laboratory.
  • FIG. 4(a) is a schematic diagram showing the distribution of an RF transmitter in an interior according to an embodiment of the present invention. As shown in Fig. 4(a), in the room of 10 m * 10 m, six RF emitters (corresponding to the figures, respectively, Tag 1, 2, 3, 4, 5, 6) are arranged.
  • Fig. 4(b) is a schematic view showing the distribution of the signal intensity of the label 5 in Fig. 4(a).
  • the intensity distribution of the signal transmitted by the RF transmitter does not decrease linearly with the distance from the transmitter, but presents a certain state distribution within a certain range around the transmitter, and the specific signal distribution See Figure 4(b) for the situation.
  • the signal intensity distribution of the set points of the other tags (Tag 1, 2, 3, 4, and 6) is similar to that of the tag 5.
  • the magnetic data distribution data of the real indoor ground is measured by using a MicroMag3 magnetic sensor chip.
  • the magnetic sensor chip can simultaneously measure magnetic field values in three directions of longitudinal, lateral and gravity on the horizontal plane, namely H X , H Y , H Z .
  • the RF receiver is used to detect the signal from the tag.
  • the geomagnetic field data in the range of 10m*10m and the tag signal distribution data of the RF transmitter are measured in the field.
  • a set of data is averaged every 0.5m, and a total of 441 sets of data are measured as a reference database and imported into the data processing unit for use in the following data analysis.
  • Fig. 5(a) is a schematic diagram showing the simulation result of positioning the target according to the RFID signal and the magnetic field signal.
  • Figure 5 (b) is a schematic diagram of the simulation results of the target positioning based on the magnetic field signal.
  • the "star” in the positioning simulation diagram represents The position of the target estimated by the algorithm of steps S302-305 in the above embodiment.
  • "Circle” black
  • “Cross” is the case of particle distribution. The contrast between the two can be clearly seen that the "star” and “circle” in Figure 5(a) have basically coincided, and the "cross” is distributed in the vicinity of "star” and “circle”.
  • the "star” in Figure 5(b) is to the right of the "circle” and is at a distance from it.
  • FIG. 5(a) determines the position of the moving target indoors more accurately than the positioning method of FIG. 5(b), and improves the utilization and convergence of the particles.
  • Figure 6(a) is a graphical representation of the error of the target based on RFID, magnetic field signal localization, and positioning based solely on magnetic field signals.
  • Figure 6(b) is a graphical representation of the time required to locate the target based on RFID, magnetic field signal localization, and localization based only on the magnetic field signal.
  • a fold line on the upper part represents an error generated only by the positioning of the magnetic field signal, and the average distance error of the positioning is about 3.3 m.
  • the following polyline represents the error caused by RFID and magnetic field signal positioning, and its positioning average distance error is about 0.7m.
  • Figure 6(b) is a graphical representation of the time required to locate the target based on RFID, magnetic field signal localization, and localization based only on the magnetic field signal.
  • the positioning is completed after the target walks twice.
  • the upper one of the broken lines represents the time required for each iteration based on the positioning of the magnetic field signal, and its running time is about 2.4 s.
  • the lower line represents the time required for each iteration based on the RFID and magnetic field signals.
  • the running time is about 1.8s.
  • the method adopted by the embodiment of the present invention can not only greatly improve the accuracy of target positioning, but also reduce the time required for positioning.
  • the indoor area can be divided into a plurality of smaller areas, and the magnetic field can be more accurately positioned more quickly.
  • the location of the WiFi node may also be used to divide the indoor area. Therefore, WiFi, IRID, Bluetooth, and other technologies can also be applied to the present invention.
  • FIG. 7 is a schematic diagram of functional modules of a cluster magnetic field positioning device according to an embodiment of the present invention.
  • the apparatus 10 for indoor positioning may include a signal receiver 11, a positioning device 12, a magnetic sensor 13, and a predictive updating device 14. among them:
  • the signal receiver 11 is used to acquire a wireless signal.
  • the magnetic sensor can measure the magnetic field signal in the room using, for example, a MicroMag3 three-axis magnetometer from PNI Sensor Corporation.
  • the positioning device 12 is a positioning device that performs a first positioning of the target based on the acquired wireless signal strength.
  • the magnetic sensor 13 is used to acquire a magnetic field signal.
  • the predictive update device 14 is configured to perform a second positioning of the target based on the range determined in the first positioning and the received magnetic field signal.
  • the predictive update device 14 may include a prediction device 141 and an update device 142. among them:
  • the predicting means 141 is configured to predict the second position of the target based on the magnetic field signal acquired by the magnetic sensor.
  • the updating device 142 is configured to update the predicted second position according to the predicted second position and the magnetic field signal acquired by the magnetic sensor.
  • FIG. 8 is a schematic diagram of a system function module for indoor positioning according to an embodiment of the present invention.
  • system 100 for indoor positioning may include the apparatus 10 and signal transmitter 20 described above for indoor positioning. among them:
  • Signal transmitter 30 is used to transmit wireless signals.
  • the wireless signals may be RFID signals, IRID signals, WIFI signals, Bluetooth signals, and the like.
  • various functions of the related functional modules may be implemented by a hardware processor and units to perform the method embodiments described above.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment. Those of ordinary skill in the art can understand and implement without deliberate labor.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Probability & Statistics with Applications (AREA)
  • Automation & Control Theory (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

簇式磁场定位的方法、装置和系统，其中，该方法包括：获取无线信号和磁场信号（S101）；根据所述无线信号对目标进行第一次定位（S102）；在第一次定位的范围内，根据所述磁场信号对所述目标进行第二次定位（S103）。由此，通过无线信号对目标进行第一次粗定位，然后在第一次定位的范围内对目标进行精确定位，减小了计算量，降低了计算的复杂程度，并提高了定位的精度。

Description

簇式磁场定位的方法、装置和系统
本发明要求在2016年01月11日提交中国专利局、申请号为201610016705.1、发明名称为“簇式磁场定位的方法、装置和系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及室内定位的技术领域，特别涉及簇式磁场定位的方法、装置和系统。
背景技术
研究证明，大型室内空间由于建筑材料和建筑结构的不同，产生了大量的磁场，这些磁场和地磁场一起构成了一个复杂的磁场图。因为建筑所用材料和建筑结构是常规的，因此建筑内的磁场图有着自己独特的规律，其所形成的室内磁场也相对稳定。这种稳定的磁场被用来进行室内定位。例如申请号为201310350209.6和201410277471.7的中国发明专利申请描述了定位技术以及定位和制图技术。利用磁场完成定位的关键是准确地辨别出磁场的特征，其所采用的技术一般基于Kalman滤波器或者是粒子滤波器。但是这两种算法的复杂度高，在室内面积较大时，所需要计算的数据较多，需要很长的计算时间，因此实时定位就很难实现。另外，在室内面积较大时，在不同的区域会出现类似的特征，这常常会导致定位的错误，而发生诸如机器人被绑架(kidnapped robot problem)等问题。另外，对于基于Kalman滤波或者粒子滤波的定位方法，需要给定目标一个初始位置，这个初始位置也需要从磁场定位外的其它技术手段获得，因此使用很不方便。
现有技术中，除了磁场定位外，由于RFID(Radio Frequency Identification，无线射频识别)技术的普遍应用，因此发明了各种基于RFID的定位技术。例如，公开号为CN 102509059 A的中国专利文献中提出了一种利用RFID进行室内定位的方法。该方法在RFID技术的基础上内嵌超宽带信号发射模块。目标通过发射RF信号和阅读器交换信息，然后阅读器发出定位指令。目标收到定位指令后，发射超宽带信号。阅读器和多个接收天线相连接，每个天线接收到超宽带信号后，采用仿真技术，模拟信号从 天线发射，最高能量聚集点就是目标位置。再例如申请号为201410201147.7的中国专利申请，其敷设固定节点以及在服务器中标注固定节点处的磁场信息(包括强度和方位)和固定节点的位置信息。当移动节点接收到固定节点的磁场信息(包括强度和方位)和位置信息后，通过接收上述服务器中所标注的固定节点的磁场信息和位置信息修正自己的位置信息。这里固定节点的磁场信息是固定的。这些技术解决了特定环境中的定位问题。但是当RFID节点大量应用时，会发生节点碰撞、阅读器碰撞以及节点和阅读器碰撞等种种信号碰撞问题，造成RFID定位的失败。
另外，现有技术中还存在利用IRID(红外识别)和WiFi等进行定位的技术。但是它们尚不能满足人们对定位精度的更高要求。
发明内容
针对现有技术存在的问题，本发明的目的是提供一种复杂度低、计算量小、精度高的室内定位方法、装置与系统。
根据本发明的一个方面，提供了一种簇式磁场定位的方法，该方法包括：获取无线信号和磁场信号；根据接收的无线信号对目标进行第一次定位；在第一次定位的范围内，根据磁场信号对目标进行第二次定位。
根据本发明的又一个方面，提供了一种簇式磁场定位的装置，该装置包括：用于接收无线信号的接收器；用于获取磁场信号的磁传感器；用于根据获取的无线信号强度，对目标进行第一次定位的定位装置；和用于根据在第一次定位确定的范围以及接收的磁场信号对目标进行第二次定位的预测更新装置。
根据本发明的另一个方面，提供了一种簇式磁场定位的系统，该系统包括：上述的用于室内定位的装置；和用于发射无线信号的信号发射器。
由此，本发明可以通过RFID信号、IRID信号、WIFI信号、蓝牙信号等无线信号对将待定位的目标所在的空间分割成一个个小空间，形成一个个族(Cluster)，以将目标进行第一次粗定位，然后在第一次定位的范围内对目标进行精确定位，减小了采用粒子滤波定位时的面积和数据，同时也避免了机器人被绑架问题，从而减小了计算量，降低了计算的复杂程度，并提高了定位的精度。
附图说明
图1为本发明一实施方式的用于簇式磁场定位的方法流程示意图；
图2为图1中对目标进行第二次定位的流程示意图；
图3为本发明另一实施方式的簇式磁场定位的方法流程示意图；
图4(a)为本发明一实施方式的RF发射器在室内分布的示意图；
图4(b)为图4(a)中Tag5的信号强度的分布情况示意图；
图5(a)为目标根据RFID信号和磁场信号定位的仿真结果示意图；
图5(b)为目标根据磁场信号定位的仿真结果示意图；
图6(a)为目标根据RFID、磁场信号定位和仅仅根据磁场信号定位产生的误差对比示意图；
图6(b)为目标根据RFID、磁场信号定位和仅仅根据磁场信号定位所需的时间对比示意图；
图7为本发明一实施方式的簇式磁场定位的装置功能模块示意图；
图8为本发明一实施方式的簇式磁场定位的系统功能模块示意图。
具体实施方式
下面结合附图对本发明的实施方式作进一步详细的说明。
图1示意性地显示了本发明一实施方式的簇式磁场定位的方法流程图。如图1所示，该方法包括：
S101：获取无线信号和磁场信号。
在本实施方式中，可以通过目标(例如推着推车的机器人)自身携带的电磁信号检测单元检测RF/IR发射器发射的信号和通过目标自身携带的磁场(例如地磁)信号检测器(例如可以采用PNI Sensor Corporation公司的MicroMag3三轴磁力计来测量室内的磁场)检测其当前位置的地磁场信息。
S102：根据所述无线信号对目标进行第一次定位。在本实施方式中，该第一次定位是根据邻近算法来确定的。邻近算法(Nearest Neighbor)是定位算法中最易理解、实现简单的一种算法。该算法一般只提供目标的相对位置信息。
通常，在系统在定位区域的许多地方布置好RF/IR发射器，且这些发射器的位置坐标是已知的。因此，当目标移动到某个发射器附近时，接收器就会接收到相应电子标签的无线射频信号，便可得知目标的大致位置。如果目标同时接收到来自几个发射器的信号，可通过比较接收信号的强度值来确定目标的位置。上述邻近算法定位易于实现，对硬件的要求也不高。因此在一些对定位精度需求不高的场合，使用该算法是非常适合的。本实施方式中就是采用将接收到的信号强度进行比较，取信号强度最大的信号发射器所辐射的信号范围为当前目标位置的粗定位区域。
S103：在第一次定位的范围内，根据所述磁场信号对所述目标进行第二次定位。
在本实施方式中，上述用于室内定位的方法尤其适用于室内的全局定 位。
图2为图1中对目标进行第二次定位的流程示意图。如图2所示，该流程包括：
S201：该第二次定位是根据所述磁场信号的强度，预测目标的第二位置。
在本实施方式中，预测目标的位置可以采用粒子滤波算法实现。粒子滤波算法中的贝叶斯估计一般由两个阶段组成，即预测和更新。算法的最终目的是求取更新后的值，也就是目标状态的后验概率密度。所以粒子滤波的最终目的也是求取目标状态的后验概率密度。
下面进行贝叶斯估计分析：假设离散系统在t时刻的状态转移方程和观测方程可以表示为：
Figure PCTCN2016103427-appb-000001
其中，xt表示所述目标在t时刻的X坐标(可以根据地图，预先标定。然后根据目标的特征，寻找目标在地图中的位置。这种标定和寻找的方法类似于GPS的方法)和位置状态，
yt表示所述目标在t时刻由观测函数计算得到的Y坐标和位置状态，
ft为关于状态xt-1在t时刻的状态转移方程函数，
vt是测量噪声，
ht是状态xt在t时刻的观测函数，
zt表示目标在t时刻磁场强度的值，
wt是观测噪声。
在本实施方式中，该状态转移方程函数表明目标当前状态Xt是由它的前一状态Xt-1和噪声决定的。在实际中因为存在取向的问题，该函数是非线性不固定的，可以通过多次测量总结得到。
该观测函数Yt是依据网格地图中信号的强度分布和目标在当前位置检测到的信号结合起来计算得到的。
根据贝叶斯理论，假设已知初始分布函数p(x0|y0)，由于目标的初始位置随机分布，所以初始分布函数为均匀分布函数，再根据查普曼-科尔莫戈罗夫等式和假设系统为一阶马尔科夫过程，可得目标状态预测方程为：
p(xt|x1：t-1)＝∫p(xt|xt-1)p(xt-1|z1：t-1)dxt-1   (2)
其中：p(xt|z1:t-1)代表目标在t时刻的后验概率密度分布,
p(xt|xt-1)代表目标在t时刻的先验概率密度函数分布。
S202：根据所述无线信号和磁场信号的强度，将预测的第二位置进行 更新。
在本实施方式中，在得到t时刻磁场强度的值zt之后，利用贝叶斯准则对状态预测值进行更新，状态更新方程为：
Figure PCTCN2016103427-appb-000002
其中，xt表示移动的目标在t时刻的坐标和位置状态(即目标坐标点和目标朝向)；zt表示该目标在t时刻磁场强度的值；p(x0|y0)表示初始分布函数。
由此可以计算出该目标的后验概率密度(即目标的当前位置)，且此迭代递归关系构成了贝叶斯估计。粒子滤波是依据大数定理采用蒙特卡洛算法来实现贝叶斯估计中的积分运算。其实质是通过粒子的位置和其不同的权重构成的随机离散测度来近似目标的后验概率密度，并且根据算法递推更新离散随机测度。本实施方式中就是利用粒子滤波算法来实现贝叶斯估计中目标后验概率的计算。
在本实施方式中，{vt,t∈N}表示测量噪声，{wt,t∈N}表示观测噪声。在射频发射接收系统中,噪声是指不携带有用信息的电信号。而此类系统中根据噪声的来源可以分为无线电噪声、工业噪声、天电噪声和内部噪声。其中内部噪声是主要噪声来源，也称为起伏噪声，是指信道内部的热噪声和器件噪声以及来自空间的宇宙噪声。这种噪声都是不规则的随机过程。例如在通信系统中，为了理论的分析、计算系统噪声，通常假定系统信道中的热噪声为高斯白噪声。所以在射频接收和发送系统中，我们假设噪声源为随机噪声和高斯白噪声结合的加性噪声。该实施方式中，加入的噪声为射频系统的随机噪声(S～U[S1，S2])和高斯白噪声
Figure PCTCN2016103427-appb-000003
加上地磁场系统中的高斯白噪声
Figure PCTCN2016103427-appb-000004
可以直接采用已有的噪声模型(高斯白噪声)来仿真实验。
S203：根据所述磁场信号的强度，对目标的位置进行多次预测和更新。
本实施方式可以通过不断的预测、更新、再预测、再更新，通过这样的训练学习，达到一定的次数以后就终止了。能大幅度提高目标定位的精度。
在本实施方式中，无线信号可以是RFID信号、IRID信号、WIFI信号和蓝牙信号等信号。对它们的定位精度不做特别的要求，目的只是划分定位区域。
在本实施方式中，定位过程分为两个阶段。
第一阶段：根据无线信号的分布和邻近算法来确定移动目标是在某个无线信号(例如RFID/IRID标签的信号)辐射范围内。因为每个RF的信号辐射范围是已知和确定的，所以可以将目标位置的范围缩小在单一RF信号辐射的范围内。
第二阶段：在上述单一RF信号辐射的范围内，再基于磁场波动的粒子滤波算法来确定移动目标的更准确的位置。由此，简化算法的复杂度，减少计算量，提高定位精度。
图3为本发明另一实施方式的簇式磁场定位的方法流程示意图。下面以为手扶推车的机器人(即上述的目标)定位为例。如图3所示，该定位方法包括：
S301：开始(初始化)。
在本实施方式中，初始的粒子均匀分布在室内，粒子的位置为
Figure PCTCN2016103427-appb-000005
粒子权重为
Figure PCTCN2016103427-appb-000006
粒子数Ns为200。
在本实施方式中，粒子是算法中的某个点，每个粒子表示目标位置在当前活动范围内位置的一种可能。
S302：推车同时接收RF发射器发射的RSSI信号与磁场强度。
S303：推车首先依据RF发射器发射的RSSI信号强度，由邻近算法确定出其在室内的大致范围，即对推车进行粗定位。
在本实施方式中，该大致范围例如可以是：在被确定的某个RF发射节点的周围多少米；也可以是以该发射节点为中心的圆区域还是其它相关的区域；还可以是某个RF信号的辐射范围等情况。
S304：依据上述所确定的范围，参照某个RF信号的辐射范围内的地磁场分布，运用粒子滤波估计出手扶推车的机器人的相对精确的位置。
在本实施方式中，在上述粗定位所得到的区域内随机采样Ns个粒子，得到一个“新”粒子集合
Figure PCTCN2016103427-appb-000007
作为对机器人当前位置的概率分布(即机器人在当前范围内位置的可能分布)的估计。
首先，采取重要性采样的方式，即：基于蒙特卡洛法的思想，利用随机采样对目标函数做近似处理。在t时刻，对于集合粒子
Figure PCTCN2016103427-appb-000008
将每个粒子的状态根据前一步粒子滤波粗定位确定的区域从
Figure PCTCN2016103427-appb-000009
更新到
Figure PCTCN2016103427-appb-000010
其次，计算重要性权重，该重要性权重的算方法见下式：
Figure PCTCN2016103427-appb-000011
其中：
Figure PCTCN2016103427-appb-000012
为目标(即手扶推车的机器人)的真实分布，而
Figure PCTCN2016103427-appb-000013
为目标建议性的分布。这两种分布是不一样的。建议性分布是靠经验推算得到，所以建议性的分布越逼近真实分布越好。一般的真实分布是靠准确测量得到的。
本实施例可以采用的数据库(该数据库是计算单元中存储的地磁场信号分布图)可以由网格地图的大小相同的3个网格地图组成，分别用，
Figure PCTCN2016103427-appb-000014
和
Figure PCTCN2016103427-appb-000015
三个磁场量值表示。这三个磁场量值都是标量，分别为地磁场场强大小、水平分量和垂直分量。传统的Hx、Hy、Hz不是标量。因为实际中的目标的移动方向是随机的，而Hx、Hy是会随着目标的移动方向改变的，这样Hx、Hy的网格分布就会变动，Hx、Hy的大小会受传感器方向的影响，进而会对后面的定位的精度产生影响。本实施方式中，
Figure PCTCN2016103427-appb-000016
和
Figure PCTCN2016103427-appb-000017
三个磁场量(其数值是标量)可以减少误差，提高了后续定位的精度。
然后，通过式(5)归一化所述重要性权重
Figure PCTCN2016103427-appb-000018
所述
Figure PCTCN2016103427-appb-000019
最后，通过式(6)计算目标准确位置：
Figure PCTCN2016103427-appb-000020
在本实施方式中，是粒子滤波算法固有的步骤，最终的目的就是通过带有权重的粒子累加去估计目标的位置。
S305：判断手扶推车的机器人是否行走。
在本实施方式中，可以通过运动状态传感器所采集的推车的运动状态来判断手扶推车的机器人是否行走。
当推车还行走时，跳转至S302。
当推车静止时，跳转至S306：结束。
图4～图6描述了实验人员在实验室内行走时的定位情况示意图。
图4(a)为本发明一实施方式的RF发射器在室内分布的示意图。如图4(a)所示，在10m*10m的室内，均匀布置了6个RF发射器(对应于图中，分别为Tag1、2、3、4、5、6)。
图4(b)为图4(a)中标签5的信号强度的分布情况示意图。如图4(b)所示，RF发射器所发射信号的强度分布不是随着距发射器的距离而线性递减的，而是在发射器周围的一定范围内呈现一定状态的分布，具体信号分布情况 可以参考图4(b)。其他标签(Tag1、2、3、4和6)设置点的信号强度分布和标签5类似。为了验证本实施方式所提出算法的可行性，在本实施方式中采用MicroMag3磁传感器芯片测量得到真实室内地磁场分布数据。该磁传感器芯片可以分别同时测得水平面上纵向、横向和重力三个方向上的磁场值，即HX、HY、HZ。采用RF接收器检测标签发出的信号。
首先实地测量了室内10m*10m范围内的地磁场数据和RF发射器的tag信号分布数据。其中，每隔0.5m测量一组数据取平均值，一共测得441组数据作为参考数据库，并导入数据处理单元，作为下面数据分析所用。
图5(a)为对目标根据RFID信号和磁场信号定位的仿真结果示意图。图5(b)为目标根据磁场信号定位的仿真结果示意图。
如图5(a)和图5(b)所示，当图3所述的目标(手扶推车的机器人)在上述室内随机行走两步后，其定位仿真图中的“星”代表根据上述实施方式中步骤S302-305的算法估计的目标所在的位置。“圈”(黑色)代表移动目标实际所处的位置。“十字”是粒子分布的情况。二者对比可以明显的看出，图5(a)中的“星”和“圈”基本已经重合，“十字”分布在“星”和“圈”的附近。图5(b)中的“星”处于“圈”的右边且与其有一段距离。虽然有些“十字”分布在“星”和“圈”的附近，但还有很多分布在远离它们的四周。这表明图5(a)中的基于RFID信号和磁场信号的定位精度比图5(b)中的仅仅基于磁场信号的定位精度要高。且图5(a)较图5(b)的定位方法更精确地确定移动目标的在室内的位置，提高了粒子的利用率和收敛性。
图6(a)为目标根据RFID、磁场信号定位和仅仅根据磁场信号定位产生的误差对比示意图。图6(b)为目标根据RFID、磁场信号定位和仅仅根据磁场信号定位所需的时间对比示意图。
在本实施方式中，经过10次仿真，均是在目标步行2次后结束定位。如图6(a)所示，上部的一根折线代表了仅仅根据磁场信号定位产生的误差，其定位平均距离误差在3.3m左右。下面的一根折线代表了根据RFID、磁场信号定位产生的误差，其定位平均距离误差在0.7m左右。
图6(b)为目标根据RFID、磁场信号定位和仅仅根据磁场信号定位所需的时间对比示意图。
在本实施方式中，经过10次仿真，均是在目标步行2次后结束定位。如图6(b)所示，上面的一根折线代表了仅仅根据磁场信号定位时每次迭代完所需的时间，其运行时间在2.4s左右。下面的一根折线代表了根据RFID、磁场信号定位时每次迭代完所需的时间，其运行时间在1.8s左右。
从上述数据可见，本发明的实施方式采取的方法不仅可以大大提高目标定位的精度，而且可以缩小定位所需的时间。
在本实施方式中，无需布置很多个RF信号发射器，同时还可改善现有技术中单纯利用RFID信号定位中信号碰撞问题。另外，可以将室内区域划分为多个更小的区域，可以更加快速地通过磁场进行较为准确的定位。需要说明的是，也可以利用WiFi节点的位置将室内的区域进行划分。因此，WiFi、IRID、蓝牙以及其它技术也可以应用于本发明中。
图7为本发明一实施方式的簇式磁场定位的装置功能模块示意图。如图7所示，用于室内定位的装置10可以包括：信号接收器11、定位装置12、磁传感器13、和预测更新装置14。其中：
信号接收器11用于获取无线信号。
在本实施方式中，磁传感器例如可以采用PNI Sensor Corporation公司的MicroMag3三轴磁力计来测量室内的磁场信号。
定位装置12是根据获取的无线信号强度，对目标进行第一次定位的定位装置。
磁传感器13用于获取磁场信号。
预测更新装置14用于根据在第一次定位确定的范围以及接收的磁场信号对所述目标进行第二次定位。
在本实施方式中，预测更新装置14可以包括：预测装置141和更新装置142。其中：
预测装置141用于根据磁传感器获取的磁场信号，预测目标的第二位置。更新装置142用于根据预测的第二位置和磁传感器获取的磁场信号，将预测的第二位置进行更新。具体方式可以参考上述方法实施例。
图8为本发明一实施方式的用于室内定位的系统功能模块示意图。如图8所示，用于室内定位的系统100可以包括上述的用于室内定位的装置10和信号发射器20。其中：
信号发射器30用于发射无线信号。无线信号可以是RFID信号、IRID信号、WIFI信号和蓝牙信号等。
本发明实施例中可以通过硬件处理器(hardware processor)和各单元来实现相关功能模块的各项功能，以执行上文所描述的方法实施例。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各 实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
以上所述的仅是本发明的一些实施方式。对于本领域的普通技术人员来说，在不脱离本发明创造构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。

Claims (14)

  1. 一种簇式磁场定位的方法，包括：
    获取无线信号和磁场信号；
    根据所述无线信号对目标进行第一次定位；
    在第一次定位的范围内，根据所述磁场信号对所述目标进行第二次定位。
  2. 根据权利要求1所述的方法，其中，所述对所述目标进行第二次定位包括：
    根据所述磁场信号的强度，至少一次预测所述目标的第二位置；和
    根据预测的所述第二次位置和所述磁场信号的强度，对预测的所述第二位置进行至少一次的位置更新。
  3. 根据权利要求1或2所述的方法，其中，所述磁场信号分别用
    Figure PCTCN2016103427-appb-100001
    和
    Figure PCTCN2016103427-appb-100002
    的磁场量值表示，其中：HX、HY、HZ分别为纵向、横向和重力三个方向上的磁场值。
  4. 根据权利要求2所述的方法，其中，所述预测所述目标的第二位置的状态预测方程是：
    p(xt|z1：t-1)＝∫p(xt|xt-1)p(xt-1|z1：t-1)dxt-1
    其中，p(xt|z1:t-1)代表目标在t时刻的后验概率密度分布,
    p(xt|xt-1)代表目标在t时刻的先验概率密度函数分布，
    Figure PCTCN2016103427-appb-100003
    为t时刻的状态转移方程和观测方程，
    其中，t表示时刻，t∈N，
    xt表示所述目标在t时刻的X坐标和位置状态，
    yt表示所述目标在t时刻由观测函数计算得到的Y坐标和位置状态，
    ft为关于状态xt-1在t时刻的状态转移方程函数，
    vt是测量噪声，
    ht是状态xt在t时刻的观测方程函数，
    zt表示目标在t时刻磁场强度的值，
    wt是观测噪声。
  5. 根据权利要求2所述的方法，其中，所述对预测的所述第二位置进行位置更新的状态更新方程是：
    Figure PCTCN2016103427-appb-100004
    其中，
    p(zt|xt)代表重要密度函数，
    p(xt|z1:t-1)代表目标在t时刻的后验概率密度分布。
  6. 根据权利要求5所述的方法，还包括：
    A.在所述第一次定位的范围内，采集NS个粒子，获取权重值为
    Figure PCTCN2016103427-appb-100005
    的粒子集合
    Figure PCTCN2016103427-appb-100006
    其中i＝1,2,3…,NS；
    B.获取所述粒子的重要性权重
    Figure PCTCN2016103427-appb-100007
    Figure PCTCN2016103427-appb-100008
    其中：
    Figure PCTCN2016103427-appb-100009
    为目标的真实分布，
    Figure PCTCN2016103427-appb-100010
    为目标建议性的分布；
    C.归一化所述重要性权重
    Figure PCTCN2016103427-appb-100011
    Figure PCTCN2016103427-appb-100012
    D.计算目标准确位置：
    Figure PCTCN2016103427-appb-100013
    Figure PCTCN2016103427-appb-100014
  7. 根据权利要求1-6任一项所述的方法，其中，
    所述无线信号包括RFID信号、IRID信号、WIFI信号和蓝牙信号。
  8. 一种簇式磁场定位的装置，包括：
    用于获取无线信号的信号接收器；
    用于获取磁场信号的磁传感器；
    用于根据获取的无线信号强度，对目标进行第一次定位的定位装置；
    用于根据在第一次定位确定的范围以及接收的磁场信号对所述目标进行第二次定位的预测更新装置。
  9. 根据权利要求8所述的装置，其中，所述预测更新装置包括：
    用于根据磁传感器获取的磁场信号，预测所述目标的第二位置的预测装置；
    用于根据预测的第二位置和磁传感器获取的磁场信号，将预测的第二位置进行更新的更新装置。
  10. 根据权利要求9所述的装置，其中，所述磁场信号分别用
    Figure PCTCN2016103427-appb-100015
    和
    Figure PCTCN2016103427-appb-100016
    的磁场量值表示，其中：HX、HY、HZ分别为纵向、横向和重力三个方向上的磁场值。
  11. 根据权利要求10所述的装置，其中，所述预测所述目标的第二位置的状态预测方程是：
    p(xt|z1：t-1)＝∫p(xt|xt-1)p(xt-1|z1：t-1)dxt-1
    其中，p(xt|z1:t-1)代表目标在t时刻的后验概率密度分布,
    p(xt|xt-1)代表目标在t时刻的先验概率密度函数分布，
    Figure PCTCN2016103427-appb-100017
    为t时刻的状态转移方程和观测方程，
    其中，t表示时刻，t∈N，
    xt表示所述目标在t时刻的X坐标和位置状态，
    yt表示所述目标在t时刻由观测函数计算得到的Y坐标和位置状态，
    ft为关于状态xt-1在t时刻的状态转移方程函数，
    vt是测量噪声，
    ht是状态xt在t时刻的观测方程函数，
    zt表示目标在t时刻磁场强度的值，
    wt是观测噪声。
  12. 根据权利要求10所述的装置，其中，所述对预测的所述第二位置进行位置更新的状态更新方程是：
    Figure PCTCN2016103427-appb-100018
    其中，
    p(zt|xt)代表重要密度函数，
    p(xt|z1:t-1)代表目标在t时刻的后验概率密度分布。
  13. 根据权利要求12所述的装置，其中所述预测更新装置配置为：
    A.在所述第一次定位的范围内，采集NS个粒子，获取权重值为
    Figure PCTCN2016103427-appb-100019
    的粒子集合
    Figure PCTCN2016103427-appb-100020
    其中i＝1,2,3…,NS；
    B.获取所述粒子的重要性权重
    Figure PCTCN2016103427-appb-100021
    Figure PCTCN2016103427-appb-100022
    其中：
    Figure PCTCN2016103427-appb-100023
    为目标的真实分布，
    Figure PCTCN2016103427-appb-100024
    为目标建议性的分布；
    C.归一化所述重要性权重
    Figure PCTCN2016103427-appb-100025
    Figure PCTCN2016103427-appb-100026
    D.计算目标准确位置：
    Figure PCTCN2016103427-appb-100027
    Figure PCTCN2016103427-appb-100028
  14. 一种簇式磁场定位的系统，包括：
    如权利要求8至13任一项所述的用于室内定位的装置；和
    用于发射无线信号的信号发射器。
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CN108318857A (zh) * 2018-02-09 2018-07-24 电子科技大学 基于分数阶傅里叶变换的多个非合作发射源被动定位方法
CN108318857B (zh) * 2018-02-09 2020-04-07 电子科技大学 基于分数阶傅里叶变换的多个非合作发射源被动定位方法

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