CN111698649A - Vehicle positioning method under GPS-assisted NLOS (non line of sight) propagation scene - Google Patents

Vehicle positioning method under GPS-assisted NLOS (non line of sight) propagation scene Download PDF

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CN111698649A
CN111698649A CN202010499631.8A CN202010499631A CN111698649A CN 111698649 A CN111698649 A CN 111698649A CN 202010499631 A CN202010499631 A CN 202010499631A CN 111698649 A CN111698649 A CN 111698649A
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vehicle
positioning
nlos
gps
vehicles
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何睿斯
赵威程
艾渤
钟章队
陈瑞凤
张皓翔
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Beijing Jiaotong University
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Beijing Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

The invention provides a vehicle positioning method under a GPS-assisted NLOS propagation scene. The method comprises the following steps: generating a simulation model for vehicle positioning, and calculating the measurement distance between vehicles by using the simulation model; calculating the estimated position of the vehicle without the GPS to be detected by using the measuring distance between the vehicles through a TDOA algorithm; calculating the positioning error of the estimated position of the vehicle without the GPS, and calculating the interruption probability according to the positioning error; determining a positioning parameter of a vehicle to be detected without a GPS, and obtaining an optimal positioning strategy adopted when the lowest interrupt probability can be reached under different scenes through comparison according to the positioning parameter; and carrying out position positioning on the vehicle to be detected without the GPS by utilizing the optimal positioning strategy. The invention provides a selection scheme of positioning strategies under different environments, further improves the precision of vehicle positioning, and makes a contribution to the realization and popularization of vehicle networking application based on accurate position information under the 5G background.

Description

Vehicle positioning method under GPS-assisted NLOS (non line of sight) propagation scene
Technical Field
The invention relates to the technical field of vehicle positioning, in particular to a vehicle positioning method under a GPS (global positioning system) -assisted NLOS (non line of sight) propagation scene.
Background
In recent years, with the rapid development of 5G technology, the internet of vehicles and related technologies have attracted extensive attention in the academic world. Especially for some applications (such as automatic driving and collision detection) that are highly dependent on vehicle position information, how to obtain the precise position of the vehicle becomes an important research direction. However, the signal propagation environment in the traffic system is complex, the communication link between vehicles is often blocked by various obstacles, and NLOS (non-line-of-sight) propagation generated by the NLOS greatly affects the accuracy of vehicle positioning, and makes positioning of the vehicle difficult.
To solve this problem, NLOS identification (non-line-of-sight identification) and NLOS submission (non-line-of-sight submission) are commonly used and effective means. Wherein, the identification identifies a link in an NLOS state; on this basis, the ranging error of the NLOS link is eliminated by the transmigration. However, the identification of NLOS identification still has an error, which limits further improvement of the positioning accuracy. Meanwhile, the study of the influence of the recognition error of the NLOS on the positioning precision is still insufficient in the current academic world, and the improvement of the positioning performance in the NLOS environment is not facilitated.
Disclosure of Invention
The embodiment of the invention provides a vehicle positioning method under a GPS-assisted NLOS propagation scene, which aims to overcome the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A vehicle positioning method under a GPS-assisted NLOS propagation scene comprises the following steps:
generating a simulation model for vehicle positioning, and calculating the measurement distance between vehicles by using the simulation model;
calculating the estimated position of the vehicle without the GPS to be detected by using the measuring distance between the vehicles through a TDOA algorithm;
calculating a positioning error of the estimated position of the vehicle to be detected without the GPS, and calculating an interruption probability according to the positioning error; determining the positioning parameters of the vehicle to be tested without the GPS, and obtaining the optimal positioning strategy adopted when the lowest interrupt probability can be reached under different scenes through comparison according to the positioning parameters;
and carrying out position positioning on the vehicle to be detected without the GPS by utilizing the optimal positioning strategy.
Preferably, the generating a simulation model of vehicle positioning, calculating the measured distance between the vehicles by using the simulation model, includes:
generating a simulation model of vehicle positioning, in which simulation model: with NgpsNumber of vehicles with GPS positioning capability, NvThe number of vehicles, whose position is known, representing the total number of vehicles is denoted NknowAnd initializing it to Nknow＝NgpsUsing the vehicle with GPS positioning capability as an anchor point for positioning the vehicle without the GPS; the NLOS recognition error is divided into a false condition and a miss condition, wherein the false condition represents that an LOS state is recognized as an NLOS state by mistake, the miss condition represents that the NLOS state is recognized as an LOS state, and the probability of the false condition is PFThe probability of miss occurrence is PMTotal probability of recognition error PE，
Figure BDA0002524307380000021
The method for calculating the measured distance between the ith vehicle and the jth vehicle comprises the following steps:
Figure BDA0002524307380000022
wherein the content of the first and second substances,
Figure BDA0002524307380000023
between vehiclesMeasured distance of, mijRepresenting the measurement error, nijRepresenting NLOS error, n ij0 in LOS environment.
Preferably, the calculating of the estimated location information of the vehicle to be tested without GPS by using the measured distance between the vehicles through the TDOA algorithm includes:
for a vehicle equipped with a vehicle-mounted GPS, acquiring position information of the vehicle through the vehicle-mounted GPS;
for a vehicle to be tested without a vehicle-mounted GPS, the vehicle to be tested establishes communication with all other vehicles within the communication range of the vehicle to be tested, the other vehicles with known positions are used as anchor points, the vehicle to be tested calculates the distance between the vehicle to be tested and the anchor points by measuring the round-trip time, the anchor points send the position information of the vehicle to be tested to the vehicle to be tested, the vehicle to be tested calculates the position information of the vehicle to be tested by utilizing the distance between the vehicle to be tested and the anchor points and the position information of the anchor points through a TDOA algorithm, and the position information of the vehicle to be tested is fed back.
Preferably, the calculating a positioning error of the estimated position of the vehicle to be measured without the GPS and calculating the outage probability according to the positioning error includes:
the Euclidean distance between the estimated position and the actual position of the vehicle to be detected without the GPS is a positioning error, and the calculation formula of the positioning error is as follows:
Figure BDA0002524307380000031
wherein e isiIndicating the positioning error of the i-th vehicle, PiRepresenting the true position coordinates of the ith vehicle,
Figure BDA0002524307380000032
estimated position coordinates for the ith vehicle;
calculating the positioning error e of all vehicles to be measured without GPSiAverage value e of (a):
Figure BDA0002524307380000033
wherein，IiIs an indicator function, I when the ith vehicle has a GPS i0, otherwise Ii＝1；
Given a maximum allowable positioning error ethWhen the distance between the estimated position and the actual position exceeds ethThen determining that the interruption occurs and the positioning can not be realized, and recording the probability of the interruption as PoutCalculating a predetermined e by the Monte Carlo methodthProbability of interruption Pout(eth)：
Figure BDA0002524307380000041
Figure BDA0002524307380000042
Is an indicator function when
Figure BDA0002524307380000043
When the temperature of the water is higher than the set temperature,
Figure BDA0002524307380000044
otherwise, the reverse is carried out
Figure BDA0002524307380000045
Preferably, the determining the positioning parameters of the vehicle to be tested without the GPS includes:
counting the probability P of NLOS propagation of the current positioning scene of the vehicle to be tested without the GPSNLOSTwo test vehicles with cameras on the roofs are deployed on the test road section, and if the cameras of the two vehicles can observe each other, the current path is considered to be an LOS path; otherwise, the route is the NLOS route, the positions of the two vehicles are continuously changed, and the operations are repeated to obtain the NLOS/LOS state information, P, of the multiple linksNLOSEstimated by the monte carlo method, namely:
Figure BDA0002524307380000046
wherein, IlinkIs an indicationA function, which is expressed as:
Figure BDA0002524307380000047
to IlinkTaking the mean value to obtain PNLOSA value of (d);
identifying each link by using NLOS identification, comparing the LOS/NLOS state information identification result of each link with the real state, and if the real state is LOS but the identification result is NLOS, generating false type errors; if the true state is NLOS, but the identification result is LOS, the true state is miss, and after the number of samples of false and miss are determined, two false identification probabilities P are obtainedFAnd PMCalculated from the following formula:
Figure BDA0002524307380000048
Figure BDA0002524307380000051
and recording the size of the communication range r of the vehicle to be tested without the GPS.
Preferably, the obtaining, according to the positioning parameter, the optimal positioning strategy adopted when the lowest outage probability can be achieved in different scenarios through comparison includes:
setting two positioning strategies of only identification and first identification and then calibration, wherein the only identification positioning strategy represents that only NLOS identification is used, signal data identified as NLOS are discarded, only LOS signals are used for positioning calculation, the first identification and then calibration strategy firstly carries out NLOS identification, then the identified NLOS data are subjected to identification to calibrate NLOS errors, and then all data are used for estimating the position of a target vehicle;
based on the principle that the probability of interruption is the minimum in different scenes, the optimal positioning policy table in different scenes is set as shown in table two:
watch two
Figure BDA0002524307380000052
According to the communication ranges r and P corresponding to the GPS-free vehicle to be testedNLOSProbability of misidentification PFAnd PMThe second table is queried to obtain the optimal positioning strategy.
The technical scheme provided by the embodiment of the invention can show that the invention provides a GPS-assisted vehicle positioning algorithm by combining NLOSidentification and verification technologies, simulates the influence of NLOS propagation on the vehicle positioning precision by utilizing the algorithm, provides comparison of vehicle positioning precision under different positioning scenes and positioning strategies, provides selection schemes of positioning strategies under different environments, further improves the vehicle positioning precision, and makes a contribution to realization and popularization of vehicle networking application based on precise position information under a 5G background.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a process flow diagram of a method for a GPS assisted collaborative vehicle positioning algorithm according to an embodiment of the present invention;
fig. 2 shows a "recognition only" positioning interruption probability under three different false recognition probabilities provided by the embodiment of the present invention.
Fig. 3 shows the probability of interruption of positioning using "calibration after identification" under three different false identification probabilities according to the embodiment of the present invention.
Fig. 4 shows that when r is 200m, the error is the sameIdentifying a different P with probabilityFAnd PMThe "recognition only" probability of location interruption is employed.
Fig. 5 shows that when r is 200m, different P s have the same misrecognition probability according to the embodiment of the present inventionFAnd PMAnd adopting the positioning interruption probability of 'recognition before calibration'.
Fig. 6 shows that when r is 500m, different P s have the same misrecognition probability according to the embodiment of the present inventionFAnd PMThe "recognition only" probability of location interruption is employed.
Fig. 7 shows that when r is 500m, different P s have the same misrecognition probability according to the embodiment of the present inventionFAnd PMAnd adopting the positioning interruption probability of 'recognition before calibration'.
Fig. 8 is a schematic diagram of a distance measurement according to an embodiment of the present invention.
Fig. 9 is a flowchart of vehicle positioning according to an embodiment of the present invention.
Fig. 10 is a schematic diagram of a positioning error according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The invention aims to research the relation between the identification error of NLOS identification and the positioning precision and accordingly improve the precision of vehicle positioning. A GPS assisted co-location algorithm for simulation is proposed, and the cases of recognition errors are classified into a false category and a miss category, wherein the false category is that an LOS state is recognized as an NLOS state by mistake, and the miss category is that the NLOS state is recognized as an LOS state by mistake. The method obtains the comparison of the positioning performance when different positioning strategies are adopted under two different types of different recognition error probabilities. On the basis, the positioning strategy with the highest positioning precision under different environments is summarized. By taking the method as a reference, higher positioning accuracy can be obtained on the premise that two recognition error probabilities are known.
The processing flow chart of the vehicle positioning method under the GPS-assisted NLOS propagation scene provided by the embodiment of the invention is shown in FIG. 1, the method can improve the vehicle positioning accuracy under the NLOS propagation scene, and the specific steps are as follows:
step S1: a simulation model of the vehicle positioning is generated.
Hair brushThe illustrative embodiments provide a GPS-assisted collaborative vehicle positioning algorithm. In the algorithm, each vehicle may be an anchor point with consistent position information or a point to be measured with unknown position, depending on whether the vehicle has the capability of acquiring its own position information through the GPS. The algorithm is described here in detail: with NgpsNumber of vehicles with GPS positioning capability, NvRepresenting the total number of vehicles. First, the number of vehicles whose own positions are known is denoted by NknowAnd initializing it to Nknow＝Ngps. And then iterate. Every iteration retrieves all vehicles, if the position of the ith vehicle is known, no operation is performed on the ith vehicle. If the position is unknown, checking whether the number of other vehicles (i.e. vehicles which can be used as anchor points) with known positions in the communication range is more than or equal to 3, if so, calculating the position of the ith vehicle by using a time difference of arrival (TDOA) algorithm, and enabling N to be Nknow＝Nknow+ 1; if not, no operation is carried out. Iterate in this way until Nknow＝NvI.e. all vehicle positions are obtained, the algorithm is stopped.
The invention specifically divides the situation of NLOS identification error into two types of false (identifying LOS state as NLOS state) and miss (identifying NLOS state as LOS state), and respectively defines the probability of occurrence of the two types of situations as PFAnd PM. And defining a probability P of total recognition errorEAnd is provided with
Figure BDA0002524307380000091
Meanwhile, two positioning strategies of 'only identification' and 'first identification and then calibration' are provided. The strategy of 'only identifying' only uses NLOS identification, discards signal data identified as NLOS and only uses LOS signal to perform positioning calculation. In addition, if the number of the finally identified LOS paths is less than 3, positioning calculation cannot be performed, and the positioning error at the moment is specified to be infinite; firstly, NLOS identification is carried out on the 'first identification and then calibration', then NLOS errors are calibrated on the data identified as NLOS through verification, and then all the data are used for a target vehicleEstimation of the vehicle position.
Then, the embodiment of the invention establishes a simulation scene for positioning the vehicle by applying the algorithm under the NLOS environment. The simulation scenario information and simulation parameters are listed in table one.
Table-simulation scenario information and simulation parameters
Figure BDA0002524307380000092
In the simulation, with PNLOSIndicating the probability of NLOS propagation, PNLOSThe larger the size, the more complex the environment in which the target is represented. And gives the error threshold e of the positioningthWhen the error of positioning exceeds ethWhen it is time, a positioning interruption is considered to have occurred. Probability of interrupt using PoutExpressed, calculated by the monte carlo method. P for the present inventionoutTo indicate the accuracy of the vehicle positioning.
The simulation of the invention is divided into three parts. In the first part, we set up three groups [ P ]F,PM]Are respectively [0,0]、[0.05,0.05]、[0.1,0.1]. Simulation parameters are set according to the table I, and three groups [ P ] are set under two strategiesF,PM]P ofoutWith PNLOSThe variation curves of (a) are shown in fig. 2 and 3. In the second part, the parameters are still set according to Table one, three groups [ P ]F,PM]Are set to [0.2,0.2 respectively]、[0,0.4]、[0.4,0]And in fig. 4 and 5, P is plotted using "identify only" and "identify first then calibrate" strategiesoutWith PNLOSThe curve of the change. In the third part, the maximum communication distance r between vehicles is increased to 500m, and the values of other parameters are consistent with the table one. Three groups of [ PF,PM]Same as the second part, PoutThe curves of (a) are shown in fig. 6 and 7.
Through analysis of simulation results, several conclusions are obtained: (1) the accuracy of NLOS identification has a large impact on the positioning performance. (2) If the probability of occurrence of NLOS is low, the strategy of 'only identification' can well reduce the error of NLOS, and the strategy of 'firstly identification and then calibration' is inferior to 'only identification' in performance due to deviation existing in mitification; however, as the probability of occurrence of NLOS increases, the number of links in LOS state is less than 3, and the performance of "identify-before-calibrate" is better than that of "identify-only". (3) When the vehicle communication range r is smaller, if the strategy of 'only identification' is adopted, the influence of false type identification errors on the positioning accuracy is larger; if "identify first and then calibrate" is used, the impact of miss is more significant. (4) The above relationship changes as the vehicle communication range r is expanded. Either strategy is adopted, miss is the most important factor limiting the positioning accuracy.
Finally, we summarize the proposed positioning strategy in table two, combining the simulation results and the related conclusions obtained by analysis. With the aid of Table II, P is obtained in the measurementFAnd PMOn the premise of (2), a positioning strategy capable of achieving higher positioning accuracy can be selected.
Table two suggested positioning strategies in different scenarios
Figure BDA0002524307380000111
Suppose a road is rectangular with a length SlWidth is SwEstablishing a two-dimensional planar coordinate system of the road and calculating the distance between the two-dimensional planar coordinate system and the road at x ∈ [0, S ]l]，y∈[0,Sw]Randomly generating N within the rectangular range ofvA non-coincident point to represent NvA vehicle, and NvFrom 1 to NvNumbering is performed. Then, by giving PgpsIn NvRandom determination of N in a vehiclegpsVehicle with GPS. The values of the above parameters are listed in table one.
Step S2: the measured distance between the vehicles is determined.
The measured distance between the ith and jth vehicles may be expressed as:
Figure BDA0002524307380000112
wherein the content of the first and second substances,
Figure BDA0002524307380000113
representing the measured distance between vehicles, mijRepresenting the measurement error, nijRepresenting the NLOS error. As can be seen from equation (1), the measured distance between vehicles is composed of three components, namely, the true distance, the measurement error, and the error due to NLOS propagation. Wherein n isij0 in LOS environment. As shown in FIG. 8, the distances between all vehicles are measured, and the measurement noise is added to generate a signal having a magnitude of Nv×NvOf the matrix R, wherein the element RijRepresents the distance between the ith and jth vehicles measured under LOS propagation, and therefore has rij＝rjiAnd r ii0. Adding NLOS error on the basis of R to obtain a matrix R with the same sizeNLOSIt represents the measurement data after being contaminated by NLOS noise. From matrix RNLOSIn accordance with a given PNLOSThe probability extraction data is used as the measured value of the NLOS path; from the matrix R, according to 1-PNLOSThe probability of LOS path.
Step S3: and positioning the vehicle.
Fig. 9 is a flowchart of vehicle positioning according to an embodiment of the present invention, and as shown in fig. 9, vehicle positioning is divided into two cases:
(1) for a vehicle equipped with GPS (or GNSS), its location is directly derived by GPS. Specifically, a GPS receiver mounted on a vehicle establishes a link with a GPS satellite, and calculates a distance between the satellite and the receiver by measuring RTT (round-trip time) of the signal between the satellite and the receiver. When the GPS receiver measures distance information between the GPS receiver and three or more GPS satellites, the position of the vehicle is calculated by a TDOA algorithm and is fed back to a vehicle-mounted display device.
(2) For a vehicle without a GPS, the position of the vehicle is obtained through the cooperative vehicle positioning algorithm provided by the invention. First, the current vehicle establishes communication with all other vehicles within its communication range, and calculates the distance between itself and the other vehicles by measuring RTT. Meanwhile, other vehicles whose positions are known (including a vehicle located by GPS and a vehicle located by this method) transmit their own positions to the vehicle currently to be located. And finally, taking other vehicles with known positions as reference points (anchor points), calculating the position information of the vehicle to be detected by using the distance information between the vehicles through a TDOA algorithm, and feeding the position information back to the vehicle-mounted display equipment.
Step S4: a positioning error is estimated.
Fig. 10 is a schematic diagram of a positioning error according to an embodiment of the present invention. As shown in fig. 10, in the present invention, the positioning error refers to the euclidean distance between the estimated position and the actual position, that is:
Figure BDA0002524307380000131
wherein e isiIndicating the positioning error of the i-th vehicle, PiRepresenting the true position coordinates of the ith vehicle,
Figure BDA0002524307380000132
is the estimated position coordinates of the ith vehicle. It should be noted that, since the present invention focuses on the positioning accuracy in the NLOS scenario, rather than the accuracy of GPS positioning, the present invention only counts the positioning error of a vehicle without GPS. That is, for each positioning, the positioning errors of all the vehicles without GPS in the system are calculated, and the average value e of the positioning errors is obtained. Specifically, e can be obtained by the following method:
Figure BDA0002524307380000133
wherein, IiIs an indicator function, I when the ith vehicle has a GPS i0, otherwise Ii＝1。
Step S5: calculating the probability of interruption Pout。
Given a maximum allowable positioning error eth(in m) when the distance between the estimated position and the actual position exceeds ethWhen the positioning is finished, the interruption is determined to occur, namely the positioning can not be realized, and the probability of the interruption is recorded as PoutAnd can be obtained by the monte carlo method, in particular:
Figure BDA0002524307380000134
wherein the content of the first and second substances,
Figure BDA0002524307380000135
is an indicator function when
Figure BDA0002524307380000136
When the temperature of the water is higher than the set temperature,
Figure BDA0002524307380000137
otherwise, the reverse is carried out
Figure BDA0002524307380000138
Thus at a defined ethProbability of interruption Pout(eth) Can be obtained by multiple experiments, and
Figure BDA0002524307380000139
taking the mean value
Figure BDA00025243073800001310
Thus obtaining the product.
Step 6: and selecting an optimal positioning strategy.
And obtaining the positioning strategy adopted when the lowest interruption probability can be reached under different scenes through comparison. The specific method comprises the following steps:
(1) the vehicle communication range is set to be 200m, and the interruption probability under different strategies is compared, namely, the method is compared with the method in the transverse direction in fig. 3 and 4. Suppose PF＞PMObserved alignment at different PNLOSInterruption probability P under next two positioning strategiesoutIs marked with a dotted line with a lower triangle, to determine when r is 200m and P isF＞PMCan enable PoutAs low as possible (i.e. as high as possible in terms of positioning accuracy) strategy; similarly, suppose PF＜PMComparing the outage probabilities for the two strategies (i.e. using the dotted line marked with a five-pointed star), we get r 200m and PF＜PMThe best positioning strategy.
(2) The above steps are repeated with the vehicle communication ranges of 300m and 500m, respectively.
(3) And summarizing the optimal positioning strategies under various scenes obtained through the steps into a form of a table II.
It should be noted that table two is not fixed here, and may change according to the positioning scenario and the positioning parameters. The invention only provides a thought and a method for improving the positioning precision, and the specific application is adjusted according to the situation.
And 7: determining location parameters
(1) Counting the probability of NLOS propagation occurring, P, of the current positioning sceneNLOS. Two test vehicles with camera devices on the roofs are deployed on the test road sections, and if the camera devices of the two vehicles can observe each other, the current path is considered to be an LOS path; otherwise, it is the NLOS path. And continuously changing the positions of the two vehicles, and repeating the operations to obtain NLOS/LOS state information of the multiple links. PNLOSCan be estimated by the monte carlo method, namely:
Figure BDA0002524307380000141
wherein, IlinkIs an indicator function, which is expressed as:
Figure BDA0002524307380000142
to IlinkTaking the average value to obtain PNLOSThe value of (c).
(2) Measuring two false recognition probabilities, i.e. PFAnd PM. For P in completion (1)NLOSThe LOS/NLOS state information of each link can be obtained at the same time during the measurement. Identifying the links by using NLOS identification, comparing the identification result of the links with the real state, and if the real state is LOS and the identification result is NLOS, generating false type errors; if the true state is NLOS, but the identification result is LOS, then miss is obtained. Samples after determination of false and missAfter this amount, PFAnd PMCan be calculated from the following formula:
Figure BDA0002524307380000151
Figure BDA0002524307380000152
(3) the size of the communication range r of the vehicle is recorded. r is usually a known parameter and only needs to be recorded before starting the actual positioning.
And 8: and obtaining the optimal positioning strategy by contrasting the optimal positioning strategy table under different scenes.
Table two suggested positioning strategies in different scenarios
Figure BDA0002524307380000153
Figure BDA0002524307380000161
P determined from the previous step in combination with Table twoFAnd PMThe magnitude relation of (1), the value of r and PNLOSThe method can determine a positioning strategy which is most suitable for the current scene, and the strategy has relatively low positioning interruption probability, namely, the positioning performance is better. The specific method comprises the following steps: firstly, finding out a corresponding r value in a table according to the communication range of the vehicle; thereafter, P measured in step 7NLOSFind the corresponding P in the tableNLOSA range; finally, P is obtained according to statisticsFAnd PMAnd comparing and recording the size relationship of the two. According to the three conditions, the positioning strategy with the highest positioning precision in the table can be determined. The strategy is selected for determination, so that the positioning precision of the vehicle can be further improved.
In summary, the invention provides a GPS-assisted vehicle positioning algorithm in combination with NLOS identification and verification technologies, and the algorithm is used for simulating the influence of NLOS propagation on the vehicle positioning accuracy, providing vehicle positioning accuracy comparison under different positioning scenes and positioning strategies, and analyzing the result. By combining simulation results and conclusions, the invention provides a selection scheme of positioning strategies under different environments, further improves the precision of vehicle positioning, and makes a contribution to the realization and popularization of vehicle networking application based on accurate position information under the 5G background.
The embodiment of the invention provides a method for improving vehicle positioning accuracy under an NLOS propagation environment by combining NLOS identification and verification technologies. In the method, the NLOS identification error occurrence conditions are further divided into two types, and the influence of the two types of conditions on the positioning accuracy is specifically researched. On the basis, the positioning strategy capable of achieving higher positioning accuracy under different vehicle communication ranges and probability of NLOS propagation is summarized and summarized into a table form for reference. By referring to the table, the positioning accuracy of the vehicle can be further improved effectively under the support of the existing NLOS identification and verification technology, and certain contribution is made to the development of the vehicle networking technology and the research and popularization of the application of the vehicle networking technology.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A vehicle positioning method under a GPS-assisted NLOS propagation scene is characterized by comprising the following steps:
generating a simulation model for vehicle positioning, and calculating the measurement distance between vehicles by using the simulation model;
calculating the estimated position of the vehicle without the GPS to be detected by using the measuring distance between the vehicles through a TDOA algorithm;
calculating a positioning error of the estimated position of the vehicle to be detected without the GPS, and calculating an interruption probability according to the positioning error; determining the positioning parameters of the vehicle to be tested without the GPS, and obtaining the optimal positioning strategy adopted when the lowest interrupt probability can be reached under different scenes through comparison according to the positioning parameters;
and carrying out position positioning on the vehicle to be detected without the GPS by utilizing the optimal positioning strategy.
2. The method of claim 1, wherein generating a simulation model of vehicle positioning, using the simulation model to calculate the measured distance between the vehicles, comprises:
generating a simulation model of vehicle positioning, in which simulation model: with NgpsNumber of vehicles with GPS positioning capability, NvThe number of vehicles, whose position is known, representing the total number of vehicles is denoted NknowAnd initializing it to Nknow＝NgpsUsing the vehicle with GPS positioning capability as an anchor point for positioning the vehicle without the GPS; the NLOS recognition error is divided into a false condition and a miss condition, wherein the false condition represents that an LOS state is recognized as an NLOS state by mistake, the miss condition represents that the NLOS state is recognized as an LOS state, and the probability of the false condition is PFThe probability of miss occurrence is PMTotal probability of recognition error PE，
Figure FDA0002524307370000011
The method for calculating the measured distance between the ith vehicle and the jth vehicle comprises the following steps:
Figure FDA0002524307370000012
wherein the content of the first and second substances,
Figure FDA0002524307370000021
representing the measured distance between vehicles, mijRepresenting the measurement error, nijRepresenting NLOS error, nij0 in LOS environment.
3. The method as claimed in claim 2, wherein the calculating of the estimated location information of the GPS-free vehicle under test by the TDOA algorithm using the measured distance between the vehicles comprises:
for a vehicle equipped with a vehicle-mounted GPS, acquiring position information of the vehicle through the vehicle-mounted GPS;
for a vehicle to be tested without a vehicle-mounted GPS, the vehicle to be tested establishes communication with all other vehicles within the communication range of the vehicle to be tested, the other vehicles with known positions are used as anchor points, the vehicle to be tested calculates the distance between the vehicle to be tested and the anchor points by measuring the round-trip time, the anchor points send the position information of the vehicle to be tested to the vehicle to be tested, the vehicle to be tested calculates the position information of the vehicle to be tested by utilizing the distance between the vehicle to be tested and the anchor points and the position information of the anchor points through a TDOA algorithm, and the position information of the vehicle to be tested is fed back.
4. The method as claimed in claim 3, wherein said calculating a positioning error of the estimated position of the GPS-less vehicle under test and calculating the outage probability based on the positioning error comprises:
the Euclidean distance between the estimated position and the actual position of the vehicle to be detected without the GPS is a positioning error, and the calculation formula of the positioning error is as follows:
Figure FDA0002524307370000022
wherein e isiIndicating the positioning error of the i-th vehicle, PiRepresenting the true position coordinates of the ith vehicle,
Figure FDA0002524307370000023
estimated position coordinates for the ith vehicle;
calculating the positioning error e of all vehicles to be measured without GPSiAverage value e of (a):
Figure FDA0002524307370000024
wherein, IiIs an indicator function, I when the ith vehicle has a GPSi0, otherwise Ii＝1；
Given a maximum allowable positioning error ethWhen estimating the distance between the position and the actual positionIon exceeds ethThen determining that the interruption occurs and the positioning can not be realized, and recording the probability of the interruption as PoutCalculating a predetermined e by the Monte Carlo methodthProbability of interruption Pout(eth)：
Figure FDA0002524307370000031
Figure FDA0002524307370000032
Is an indicator function when
Figure FDA0002524307370000033
When the temperature of the water is higher than the set temperature,
Figure FDA0002524307370000034
otherwise, the reverse is carried out
Figure FDA0002524307370000035
5. The method of claim 4, wherein said determining the positioning parameters of the GPS-less vehicle under test comprises:
counting the probability P of NLOS propagation of the current positioning scene of the vehicle to be tested without the GPSNLOSTwo test vehicles with cameras on the roofs are deployed on the test road section, and if the cameras of the two vehicles can observe each other, the current path is considered to be an LOS path; otherwise, the route is the NLOS route, the positions of the two vehicles are continuously changed, and the operations are repeated to obtain the NLOS/LOS state information, P, of the multiple linksNLOSEstimated by the monte carlo method, namely:
Figure FDA0002524307370000036
wherein, IlinkIs an indicator function, which is expressed as:
Figure FDA0002524307370000037
to IlinkTaking the mean value to obtain PNLOSA value of (d);
identifying each link by using NLOS identification, comparing the LOS/NLOS state information identification result of each link with the real state, and if the real state is LOS but the identification result is NLOS, generating false type errors; if the true state is NLOS, but the identification result is LOS, the true state is miss, and after the number of samples of false and miss are determined, two false identification probabilities P are obtainedFAnd PMCalculated from the following formula:
Figure FDA0002524307370000041
Figure FDA0002524307370000042
and recording the size of the communication range r of the vehicle to be tested without the GPS.
6. The method as claimed in claim 5, wherein the step of obtaining the optimal positioning strategy for achieving the lowest interruption probability under different scenarios by comparison according to the positioning parameters comprises:
setting two positioning strategies of only identification and first identification and then calibration, wherein the only identification positioning strategy represents that only NLOS identification is used, signal data identified as NLOS are discarded, only LOS signals are used for positioning calculation, the first identification and then calibration strategy firstly carries out NLOS identification, then the identified NLOS data are subjected to identification to calibrate NLOS errors, and then all data are used for estimating the position of a target vehicle;
based on the principle that the probability of interruption is the minimum in different scenes, the optimal positioning policy table in different scenes is set as shown in table two:
watch two
Figure FDA0002524307370000043
Figure FDA0002524307370000051
According to the communication ranges r and P corresponding to the GPS-free vehicle to be testedNLOSProbability of misidentification PFAnd PMThe second table is queried to obtain the optimal positioning strategy.
CN202010499631.8A 2020-06-04 2020-06-04 Vehicle positioning method under GPS-assisted NLOS (non line of sight) propagation scene Pending CN111698649A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104833953A (en) * 2015-05-11 2015-08-12 中国民用航空总局第二研究所 Multipoint positioning monitoring system and multipoint positioning monitoring method in airport non-line-of-sight (NLOS) channel environment
CN107124762A (en) * 2017-04-26 2017-09-01 玉林师范学院 A kind of wireless location method of efficient abatement non-market value

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104833953A (en) * 2015-05-11 2015-08-12 中国民用航空总局第二研究所 Multipoint positioning monitoring system and multipoint positioning monitoring method in airport non-line-of-sight (NLOS) channel environment
CN107124762A (en) * 2017-04-26 2017-09-01 玉林师范学院 A kind of wireless location method of efficient abatement non-market value

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ISMAIL GUVENC; CHIA-CHIN CHONG; FUJIO WATANABE: "NLOS Identification and Mitigation for UWB Localization Systems", 《2007 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE》 *
STEFANO MARANÒ; WESLEY M. GIFFORD; HENK WYMEERSCH; MOE Z. WIN: "NLOS identification and mitigation for localization based on UWB experimental data", 《 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS》 *
刁虹雪: "基于NLOS误差识别与消除的TOA无线定位算法研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 *

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