CN104181500A - Real-time locating method based on inertia information and chance wireless signal characteristics - Google Patents
Real-time locating method based on inertia information and chance wireless signal characteristics Download PDFInfo
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- 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
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Abstract
The invention provides a real-time locating method based on inertia information and chance wireless signal characteristics. The real-time locating method based on inertia information and chance wireless signal characteristics comprises the steps that under the condition of an initial position, the motion state of a locating target is detected through a sensor of a locating terminal, and then the motion direction and the chance wireless signal characteristics of the current position are obtained; the possible position and the historical route of the locating target can be corrected through an improved particle filter method by combining inertia information and chance wireless signal characteristics, and then locating results are output in real time. According to the real-time locating method based on inertia information and chance wireless signal characteristics, in order to guarantee that the accuracy of a signal map established in real time, it is required that the locating target returns to the position where the locating target passes at one historical moment during locating, and therefore inertia drift is successively corrected. Compared with the prior art, the real-time locating method based on inertia information and chance wireless signal characteristics has the advantages that advanced exploration of the environment of the locating target is not needed, a high precision requirement can be met just through a common inertia sensor and a signal collecting unit of an ordinary intelligent terminal, implementation is successful, and the calculation amount is small.
Description
Technical field
The present invention relates to a kind of localization method of wireless communication field, especially relate to a kind of real-time location method based on inertia information and chance wireless signal feature.
Background technology
Be accompanied by the universal of mobile device and personal device, location technology becomes the current and following popular research field.Positioning system can judge the positional information of terminal device, positional information is used for to location-based service, as navigation, tracking, monitoring etc. simultaneously.Divide according to usable range, location-based service mainly comprises outdoor positioning application and indoor positioning application.
In outdoor positioning system, conventionally use GPS (GPS) or other global position systems to position.Gps system can be good at the individual locating information that provides outdoor, and utilizing the advantage that GPS positions is that satellite effective coverage range is large, and location navigation signal is free.But the line-of-sight transmission of signal between GPS dependence satellite and recipient, and the wireless signal of gps satellite transmitting is too faint, cannot penetrate the barriers such as most buildings, cause indoor in the situation that, gps system location is inaccurate, and the cost of locating terminal is higher.
Carry out indoor because the business of radio communication has more than 70%, indoor positioning skill receives publicity day by day in recent years.Indoor locating system is for providing the indoor location information of individual and equipment.Precision and calculating convergence time are considered to the most important factor of location technology, and oneself has a lot of indoor positioning technology now, such as infrared indoor location technology, REID, WLAN (wireless local area network) location technology, dead reckoning etc.
Now the principal feature of these several technology is briefly listed below:
1) infrared indoor location technology
Infrared (Infrared) is the technology the earliest for indoor positioning, and the principle of infrared ray indoor positioning technological orientation is that the infrared-ray of infrared ray IR identification transmissions modulation, positions by being arranged on indoor optical sensor reception.Although infrared ray has relatively high indoor position accuracy, because light can not pass barrier, make the infrared-ray only can line-of-sight propagation.Shorter this two large major defect of straight line sighting distance and transmission range makes the poor effect of its indoor positioning.In mark is placed on pocket or while having wall etc. to block, just cannot normally work; And need to be in each room, corridor installs receiving antenna, cost is higher; Infrared ray is easily disturbed and causes positioning precision to decline by the light in fluorescent light or room.Therefore, infrared ray is only suitable for short distance and propagates, and accurately on location, is having limitation.
2) radio-frequency (RF) identification (RFID) technology
Radio frequency (Radio Frequency) is also usually used in individual indoor positioning (IPS).REID utilizes RF-wise to carry out contactless bidirectional communication data exchange to reach the object of identification and location.This technical role distance range is larger, and can in several milliseconds, obtain the locating information of centimetre-sized precision, and owing to not relying on visible ray, the scope of application is wider.But because equipment manufacturing cost is higher and relate to privacy concern, and easily affected by metal or water environment, this localization method is not still used on a large scale, only under special scenes, play a role.
3) WLAN (wireless local area network) (WLAN) location technology
WLAN (wireless local area network) (WLAN) is based on 1EEE802.11 agreement, it is the computer local network of making transmission medium with wireless channel, it is the product that computer network combines with wireless communication technology, it is using wireless multiple access channel as transmission medium, the function of traditional cable LAN is provided, can make user real realize at any time, everywhere, random Broadband Network Access.The demand of the instantaneity of mobile subscriber to information and on the spot property is more and more stronger, and this provides wide development space just to the location-based service based on wlan system.In wlan system, location technology mainly contains the technology such as triangle location, signal intensity location based on RSSI (Received Signal Strength Indication, received signal strength indicator) or TOA/TDOA/AOA.Wherein, signal intensity location technology mainly comprises two class methods such as signal intensity fingerprint/signal propagation model location.There are a lot of famous WLAN positioning systems, as signal intensity and signal to noise ratio (S/N ratio) signal-to-noise are used for triangle location technology by RADAR system, by setting up radio-frequency channel propagation model, multiple WLAN access points (APs) can be oriented mobile base station according to nearest neighbours in signal space (NNSS) location algorithm.CAMPASS system, by WLAN infrastructure and digital compass, provides low cost and relatively high-precision positioning service for carrying the user of wlan device.
4) dead reckoning (Dead Reckoning)
Dead reckoning is a kind of very important airmanship, and Dead Reckoning originates from the 17th century navigation, refers to that the fixed point of being known by oneself goes out the method for current position with compass and dead reckoning; According to the position of measuring last time, and working direction, speed, time, distance just can calculate current positional information.Physical characteristics based on different and applied environment, dead reckoning sensor can combine mutually realizes different allocation plans, as the inertial navigation system of gyroscope and accelerometer combination, magnetometer and accelerometer composition without drift localization method, the remaining localization method of gyroscope, magnetometer and accelerometer unit etc.Dead Reckoning has higher precision on short distance location, but has the shortcoming of cumulative errors.
Summary of the invention
As described in the background art, for deficiency of the prior art, the object of this invention is to provide a kind of real-time location method based on inertia information and chance wireless signal feature, the method is without the environmental data of navigation scenarios is carried out to typing in advance; Utilize various chance wireless signals in scene as the calibration foundation of inertial navigation, can solve and use inertial navigation system to cause cumulative errors excessive, the problem that cannot use for a long time; Simultaneously also owing to using multiple chance wireless signal as calibration criterion, therefore can overcome in classic method, use that single wireless signal may cause as calibration criterion in some problem of criterion misalignment in particular cases.
Content of the present invention comprises four major parts: input parameter, algorithm flow, core algorithm, output parameter.
(1) input parameter
Input parameter of the present invention includes but not limited to: inertia information, chance wireless signal characteristic information, initial position message.
Wherein, inertia information includes but not limited to: the compass heading information of the terminal that is positioned, the acceleration of motion information of locating terminal.Chance wireless signal characteristic information comprises what any end-probing that can be positioned arrived, has the wireless signal that uprises spatial correlation when low, for example Wi-Fi signal, cellular basestation signal, radiated television signal etc.
(2) algorithm flow
This algorithm flow as shown in Figure 1, is mainly divided into following step:
1) adopt reliable fashion if gps signal or artificial assigned address are as reference position, and input the each mobile mean distance of localizing objects, i.e. step-length information, initialization positioning system; .
2) motion state of the acceleration transducer Monitoring and Positioning target by locating terminal, if localizing objects moves, utilize wireless signal sensor to obtain chance wireless signal characteristic information, utilize the compass of locating terminal to obtain the roughly direction of this motion simultaneously, and perform step 3), otherwise return to step 2);
3) inertia information locating terminal being collected and chance wireless signal feature, upgrade the state of population as the activation bit of particle filter, calculate the possible position of current localizing objects and upgrade its historical track by population simultaneously, output positioning result, gets back to step 2).
(3) core algorithm
Core algorithm of the present invention is the particle filter method after improving, by the particle of simulation some, and weight to particle and state are more newly arrived and are estimated the position of terminal, this particle filter method comprises following four steps: particle initialization, particle state upgrade, particle weight is upgraded, particle resampling.
1) particle initialization
Particle initialization is taking reference position given or that measure as the center of circle, the particle of some is randomly dispersed near this center of circle with the probability successively decreasing from the near to the remote from the center of circle, for each particle, taking the step-length in input parameter as probability distribution center, set a random and mutually different feature step-length, and the foundation that a random compass deviation amount is upgraded as particle state is additionally set.
2) particle state upgrades
Particle state renewal process is as follows: utilize chance wireless signal characteristic information that locating terminal measures to upgrade the current chance wireless signal characteristic information of all particles; The direction of motion information of utilizing locating terminal to measure, in conjunction with feature step-length and the feature compass deviation amount of each particle, after adding certain random zero mean Gauss disturbance, infers each particle application flight path, with this positional information of new particle more.
Wherein infer that by flight path the method for calculating particle current location is as follows: establish the angle that the directional information collecting is direction of motion and magnetic north direction, be made as θ, the feature step-length that makes each particle is l
i, feature compass deviation amount is θ
i, the random step-length disturbance generating is Δ l, and the disturbance of compass deviation amount is Δ θ, and particle eve present position coordinate is (x, y), more after new state, the position of particle (x ', y ') be:
3) particle weight is upgraded
Particle weight is upgraded and is carried out with following method: by map of living in is carried out to Virtual space division, the map partitioning at place, location is gone out to the virtual lattice that take of some squares of tessellate, for successively falling into the same particle that takies lattice, use similarity (for example cosine similarity) to calculate the successively similarity of twice chance wireless signal feature of this particle, if its similarity thinks that higher than a certain decision threshold this particle is a reentry particle, given high weight, if otherwise particle do not enter unified take lattice or enter same take lattice but similarity lower than setting threshold, this particle is labeled as to non-reentry particle, and weight is set to low weight.
Calculate similarity and upgrade the detailed process of weight as described below: establish certain successively enter the same particle that takies lattice its successively the received signals fingerprint vector of twice be (s
1, s
2, s
3..., s
m) and (s '
1, s '
2, s '
3..., s '
m), in vector, the value on the i of position is that the signal intensity of i signal source, if do not exist the zero setting cover of vector from this signal source in a certain vector from the signal source of two signal vectors concentrated.Thus, the cosine similarity ρ of signal vector calculates as follows:
the ρ calculating more approaches 1, illustrates that two signal vectors are more similar, more approaches 0 two signal vector difference larger.In the time that ρ is greater than a certain empirical predetermined threshold value, think that this particle moves middle historical position of having passed through it this this, think that this particle is a reentry particle, now with formula δ=e
-1/ log ρ calculates to obtain this particle weight δ now.
4) particle resampling
Particle resampling is carried out with following method: in particle weight step of updating, add up reentry particle number, when accounting for, reentry particle when total population ratio exceeds a certain threshold value, carries out resampling, when resampling, the particle of weight large (little) is replicated propagation with higher (low) probability becomes particle of new generation, the original position that the new particle generating is in parent particle is constant, weight is all reset to 1/N (N is total population), and feature step-length and feature compass deviation amount are that parent particle characteristics step-length and feature compass deviation amount add a zero-mean Gauss disturbance.
What need particularly point out is, for each position fixing process, localizing objects need to be got back to the position once living through at least one times, by returning historical position, select and can correctly represent the particle of this closed loop path and give its high weight, and then can correct current positioning result and revise its historical track by Weighted Average Algorithm.
Described Weighted Average Algorithm utilizes the historical position of particle, calculates each and walks corresponding population possible position pointed, and the weight of each step is all determined by the particle weight before last resampling.
Wherein, utilize the detailed process of Weighted Average Algorithm renewal particle position as follows: the current location of establishing N particle is (x
i, y
i), the current weight of each particle is δ
i, the target location calculating by this particle cloud is so:
in like manner can utilize the historical position of each particle and the right value update historical track of current this particle.
(4) output parameter
Output parameter of the present invention is, the positional information of locating terminal and historical track.
(5) other
The equipment that this method is applied to includes but not limited to have handheld device, background process server, computer and other video display apparatus of various sensors.
Brief description of the drawings
Fig. 1: location algorithm process flow diagram
Fig. 2: Experimental Area signal
Fig. 3: experiment route
The pure inertia track of Fig. 4
Route after Fig. 5 corrects
The contrast of Fig. 6 error
The contrast of Fig. 7 cumulative errors
Instantiation
Real-time location method based on inertia information and chance wireless signal feature of the present invention, is now further elaborated with the indoor positioning test case in Beijing University of Post & Telecommunication's the 3rd teaching building.The present invention is generally suitable for the application scenarios with essential characteristic in this example.
In this example, in Beijing University of Post & Telecommunication's the 3rd teaching building, in ground floor hall, gps signal is accepted difficulty, but indoor opportunity signal is abundant, stands firm a bit, utilizes locating terminal to record opportunity signal overview as shown in table 1 below:
Table 1 wireless signal is summarized
| Wireless signal type | Independent signal number |
| Wi-Fi | 14 |
| Cellular basestation signal | 6 |
Beijing University of Post & Telecommunication the 3rd teaching hall, bottom of the building building planimetric map is as shown below, and this Experimental Area illustrates by dotted line frame, as shown in Figure 2:
Test starts, and input parameter is as shown in table 2:
Table 2 output parameter
| Parameter type | Parameter values |
| Default user's step-length | 0.65m |
| Location starting point longitude and latitude | 116.363382,39.966199 |
| Primary distribution radius | 10m |
| Simulation particle number | 5000 |
In location algorithm flow process, first with location starting point (116.363382,39.966199) be the center of circle, in distribution radius (10m), with 5000 particles of the random placement of probability that successively decrease from the near to the remote from the center of circle, for each particle, taking the step-length in input parameter (0.65m) as Normal probability distribution average, taking 2 as variance, for each particle is set random non-negative feature step-length, and a 0 average Gaussian random variable is additionally set as compass deviation amount, completes the initialization of particle cloud.
Experimenter carries locating terminal and moves subsequently, the acceleration change that locating terminal causes each movement is monitored, normal acceleration is carried out to Moving Window filtering, identify the behavior of taking a step each time, and in the time that the behavior of at every turn taking a step occurs, record current chance wireless signal characteristic information, and the terminal direction of motion that now compass detector detects towards, following table 3 is the result that in an experiment, a certain secondary data collects:
Table 3 experimental data example
| Signal identification | Sampling numerical value | Signal identification | Sampling numerical value |
| Direction signal | 92 | 02:06:03:40:55:01 | -77 |
| 58:66:ba:77:34:b0 | -88 | 02:06:03:40:55:00 | -76 |
| 58:66:ba:77:35:50 | -86 | 58:66:ba:77:17:30 | -76 |
| 58:66:ba:94:52:10 | -86 | 58:66:ba:94:59:30 | -74 |
| 58:66:ba:94:5b:30 | -85 | (4138，313) | -95 |
| 58:66:ba:94:53:d0 | -85 | (4138，312) | -101 |
| 58:66:ba:94:96:50 | -81 | (4138，58677) | -93 |
| 0c:da:41:1e:22:30 | -80 | (4138，424) | -98 |
| 58:66:ba:77:36:f0 | -78 | (4140，2) | -95 |
| 58:66:ba:77:33:10 | -78 | (4138，36431) | -95 |
| 58:66:ba:94:58:10 | -77 | ? | ? |
The directional information collecting is the angle of direction of motion and magnetic north direction, is made as θ, and the feature step-length that makes each particle is l
i, feature compass deviation amount is θ
i, the random step-length disturbance generating is Δ l, and the disturbance of compass deviation amount is Δ θ, and particle eve present position coordinate is (x, y), more after new state, the position of particle (x ', y ') be:
The route that this experiment is chosen as shown in Figure 3.
The track that pure-inertial guidance obtains as shown in Figure 4.
Can be seen by Fig. 4, have larger inertia drift without the track of correcting, along with the carrying out of navigation gone far gradually from actual position.Inertial navigation through the route after the rectification of this method as shown in Figure 5.
Can be seen by Fig. 5, add correction algorithm track afterwards, in the time getting back to historical position, there is obvious rectification in navigation path.The error comparison diagram that lower Fig. 6 is two kinds of air navigation aids, Fig. 7 is cumulative errors comparison diagram.
Can be seen by Fig. 6, Fig. 7, this location algorithm makes positioning error be compared to conventional inertia navigation obvious lifting, and the step of returning history point in this algorithm has obvious effect for reducing inertia drift.
Following table 4 has been shown average and standard deviation and the location cumulative errors of the positioning error of two kinds of method generations, can apparently find out, this method, than pure inertial positioning, is all improved in error mean and standard deviation.
The contrast of table 4 error
In sum, we can see, the present invention can effectively correct the inertia skew that inertial navigation brings, and is that error mean or error to standard deviation all will significantly be better than traditional inertial navigation.
Claims (8)
1. the real-time location method based on inertia information and chance wireless signal feature, comprises input parameter, core algorithm, algorithm flow, output parameter.
2. a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, its input parameter includes but not limited to: inertia information, chance wireless signal characteristic information, initial position message, wherein:
1) the inertia information in input parameter, is characterized in that, inertia information include but not limited to be positioned the compass heading information of terminal, the acceleration of motion information of locating terminal;
2) the chance wireless signal in input parameter, is characterized in that, comprises what any end-probing that can be positioned arrived, has the wireless signal that uprises spatial correlation when low, for example Wi-Fi signal, cellular basestation signal, radiated television signal etc.;
3) initial position message in input parameter can be the coordinate information of the latitude and longitude information of locating terminal or relatively known reference point.
3. the core algorithm of a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, it is characterized in that, utilize the particle filter method after improving to infer that in conjunction with flight path realizing real-time positional information infers and correct, use particle filter by simulating the particle of some, weight and more newly arriving of state by particle are estimated the position of terminal, this particle filter method comprises following three steps: particle state upgrades, particle weight is upgraded, particle resampling; Wherein:
1) particle of the simulation some in the core algorithm of location, it is characterized in that, taking reference position given or that measure as the center of circle, the particle of some is randomly dispersed near this center of circle with the probability successively decreasing from the near to the remote from the center of circle, for each particle, taking the step-length in input parameter as probability distribution center, set a random and mutually different feature step-length, and the foundation that a random compass deviation amount is upgraded as particle state is additionally set;
2) particle state of location in core algorithm upgrades, and it is characterized in that, utilizes chance wireless signal characteristic information that locating terminal measures to upgrade the current chance wireless signal characteristic information of all particles; The direction of motion information of utilizing locating terminal to measure, in conjunction with feature step-length and the feature compass deviation amount of each particle, after adding certain random zero mean Gauss disturbance, infers each particle application flight path, with this positional information of new particle more;
3) the particle weight in the core algorithm of location is upgraded, it is characterized in that, it carries out with following method: by map of living in is carried out to Virtual space division, the map partitioning at place, location is gone out to the virtual lattice that take of some squares of tessellate, for successively falling into the same particle that takies lattice, (for example use similarity, cosine similarity) calculate the successively similarity of twice chance wireless signal feature of this particle, if its similarity thinks that higher than a certain decision threshold this particle is a reentry particle, given high weight, if otherwise particle do not enter unified take lattice or enter same take lattice but similarity lower than setting threshold, this particle is labeled as to non-reentry particle, and weight is set to low weight,
4) the particle resampling in the core algorithm of location, it is characterized in that, it carries out with following method: 3) add up reentry particle number in described particle weight step of updating, when accounting for, reentry particle when total population ratio exceeds a certain threshold value, carries out resampling, when resampling, the particle of weight large (little) is replicated propagation with higher (low) probability becomes particle of new generation, the original position that the new particle generating is in parent particle is constant, weight is all reset to 1/N (N is total population), feature step-length and feature compass deviation amount are that parent particle characteristics step-length and feature compass deviation amount add a zero-mean Gauss disturbance.
4. the algorithm flow of a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, is characterized in that, carries out as follows:
1) adopt reliable fashion if gps signal or artificial assigned address are as reference position, and input the each mobile mean distance of localizing objects, i.e. step-length information, initialization positioning system;
2) motion state of the acceleration transducer Monitoring and Positioning target by locating terminal, if localizing objects moves, utilize wireless signal sensor to obtain chance wireless signal characteristic information, utilize the compass of locating terminal to obtain the roughly direction of this motion simultaneously, and perform step 3), otherwise return to step 2);
3) locating terminal is collected inertia information and chance wireless signal feature, upgrade the state of population as the activation bit of particle filter, calculate the possible position of current localizing objects and upgrade its historical track by population simultaneously, output positioning result, gets back to step 2).
5. the algorithm flow of a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, it is characterized in that, for each position fixing process, localizing objects need to be got back to the position once living through at least one times, by returning historical position, select and can correctly represent the particle of this closed loop path and give its high weight, and then can correct current positioning result and revise its historical track by Weighted Average Algorithm.
6. in the algorithm flow of a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 4, described is corrected current positioning result and is revised its historical track by Weighted Average Algorithm, it is characterized in that, pass through weighted mean, utilize the historical position of particle, calculate each and walk corresponding population possible position pointed, the weight of each step is all determined by the particle weight before last resampling.
7. the output parameter of described a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, can be the real-time position information of locating terminal, and the historical track information of locating terminal.
8. a kind of real-time location method based on inertia information and chance wireless signal feature according to claim 1, it is characterized in that, the equipment being applied to includes but not limited to have handheld device, background process server, computer and other video display apparatus of various sensors.
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