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

Abstract

The present invention provides a vehicle positioning method in a GPS-assisted NLOS propagation scenario. The method includes: generating a simulation model of vehicle positioning, and using the simulation model to calculate a measurement distance between vehicles; using the measurement distance between vehicles to calculate an estimated position of a vehicle to be measured without GPS through a TDOA algorithm; The positioning error of the estimated position of the vehicle, and the interruption probability is calculated according to the positioning error; the positioning parameters of the vehicle to be tested without GPS are determined, and the optimal positioning used when the lowest interruption probability can be obtained in different scenarios is obtained through comparison according to the positioning parameters. Strategy; use the best positioning strategy to position the vehicle to be tested without GPS. The invention provides a selection scheme of positioning strategies in different environments, further improves the accuracy of vehicle positioning, and contributes to the realization and promotion of Internet of Vehicles applications based on precise position information in the context of 5G.

Description

Vehicle localization method in GPS-assisted NLOS propagation scenario

technical field

The invention relates to the technical field of vehicle positioning, in particular to a vehicle positioning method in a GPS-assisted NLOS propagation scenario.

Background technique

In recent years, with the rapid development of 5G technology, the Internet of Vehicles and related technologies have attracted extensive attention from the academic community. Especially for some applications that are highly dependent on vehicle position information (such as automatic driving and collision detection), how to obtain the precise position of the vehicle has become an important research direction. However, the signal propagation environment in the traffic system is complex, and the communication link between vehicles is often blocked by various obstacles. The resulting NLOS (non-line-of-sight, non-line-of-sight) propagation is very accurate for vehicle positioning. It will cause a great impact and make it difficult to locate the vehicle.

To solve this problem, NLOS identification (non-line-of-sight identification) and NLOS mitigation (non-line-of-sight mitigation, non-line-of-sight calibration) are commonly used and effective means. Among them, identification identifies the link in the NLOS state; on this basis, mitigation eliminates the ranging error of the NLOS link. However, there are still errors in the identification of NLOS identification, which limits the further improvement of positioning accuracy. At the same time, at this stage, the research on the influence of NLOS recognition errors on the positioning accuracy is still insufficient, which is not conducive to the improvement of the positioning performance in the NLOS environment.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a vehicle positioning method in a GPS-assisted NLOS propagation scenario to overcome the problems of the prior art.

In order to achieve the above objects, the present invention adopts the following technical solutions.

A vehicle positioning method in a GPS-assisted NLOS propagation scenario, comprising:

generating a simulation model of vehicle positioning, and using the simulation model to calculate the measurement distance between vehicles;

Using the measured distance between vehicles to calculate the estimated position of the vehicle to be tested without GPS through the TDOA algorithm;

The positioning error of the estimated position of the vehicle to be tested without GPS is calculated, and the interruption probability is calculated according to the positioning error; the positioning parameters of the vehicle to be tested without GPS are determined. The best positioning strategy to use when reaching the lowest probability of outage;

Use the optimal positioning strategy to position the vehicle to be tested without GPS.

Preferably, the simulation model for generating vehicle positioning, and the measurement distance between vehicles is calculated by using the simulation model, including:

Generate a simulation model of vehicle positioning, in the simulation model: use N _gps to represent the number of vehicles with GPS positioning capability, N _v to represent the total number of vehicles, and the number of vehicles whose positions are known is recorded as N _know , and initialized to N _know = N _gps , the vehicle with GPS positioning capability is used as the anchor point for positioning the vehicle without GPS; the NLOS recognition error is divided into two cases: false and miss, the false case means that the LOS state is incorrectly identified as the NLOS state, The miss condition indicates that the NLOS state is identified as the LOS state, the probability of false occurrence is P _F , the probability of miss occurrence is _PM , and the total probability of identification error _PE ,

The calculation method of the measured distance between the i-th vehicle and the j-th vehicle is:

表示车辆之间的测量距离，m_ij表示测量误差，n_ij表示NLOS误差，n_ij在LOS环境下为0。in,

represents the measurement distance between vehicles, m _ij represents the measurement error, n _ij represents the NLOS error, and n _ij is 0 in the LOS environment.

Preferably, using the measured distance between vehicles to calculate the estimated position information of the vehicle to be measured without GPS through the TDOA algorithm, including:

For vehicles equipped with on-board GPS, obtain the location information of the vehicle through on-board GPS;

For a vehicle to be tested without on-board GPS, the vehicle to be tested establishes communication with all other vehicles within its communication range, and uses other vehicles with known positions as anchor points. The vehicle to be tested calculates the distance between itself and the anchor point by measuring the round-trip time. The anchor point sends its own position information to the vehicle to be tested. The vehicle to be tested uses the distance between itself and the anchor point and the position information of the anchor point to calculate the position information of the vehicle to be tested through the TDOA algorithm, and calculates its position. The information is fed back on the on-board display device.

Preferably, the calculating the positioning error of the estimated position of the vehicle to be measured without GPS, and calculating the interruption probability according to the positioning error, including:

The Euclidean distance between the estimated position and the actual position of the vehicle to be tested without GPS is the positioning error, and the calculation formula of the positioning error is:

为第i辆车的估计位置坐标；Among them, e _i represents the positioning error of the i-th vehicle, P _i represents the real position coordinates of the i-th vehicle,

is the estimated position coordinates of the i-th vehicle;

Find the average value e of the positioning errors e _i of all vehicles to be tested without GPS:

Wherein, I _i is an indicator function, I _i =0 when the i-th vehicle has GPS, otherwise I _i =1;

Given a maximum allowable positioning error e _th , when the distance between the estimated position and the actual position exceeds e _th , it is determined that there is an interruption and the positioning cannot be achieved, and the probability of interruption is recorded as P _out , which is calculated by the Monte Carlo method. Outage probability P _out (e _th ) under _eth of :

是一个指示函数，当

时，

反之

is an indicator function, when

hour,

on the contrary

Preferably, the determining the positioning parameters of the vehicle to be measured without GPS includes:

Calculate the probability P _NLOS of NLOS propagation in the current positioning scene of the vehicle to be tested without GPS, and deploy two test vehicles with cameras on the roofs in the test section. If the cameras of the two vehicles can observe each other, then It is considered that the current path is the LOS path; otherwise, it is the NLOS path, and the positions of the two vehicles are constantly changed, and the above operations are repeated to obtain the NLOS/LOS state information of multiple links. The P _NLOS is estimated by the Monte Carlo method, that is, :

Among them, I _link is an indicator function, which is expressed as:

Take the mean value of I _link to get the value of P _NLOS ;

Use NLOS identification to identify each link, and compare the LOS/NLOS status information identification result of each link with the real status. If the real status is LOS, but the identification result is NLOS, a false type error occurs; The true state is NLOS, but the recognition result is LOS, then it is miss. After determining the number of false and miss samples, the two _{misrecognition} probabilities _PF and PM are calculated by the following formulas:

Record the size of the communication range r of the vehicle to be tested without GPS.

Preferably, the optimal positioning strategy adopted when the lowest interruption probability can be achieved in different scenarios is obtained through comparison according to the positioning parameters, including:

Two positioning strategies are set: identification only and first identification and then calibration. The identification only positioning strategy means that only NLOSidentification is used, the signal data identified as NLOS is discarded, and only the LOS signal is used for positioning calculation, and the first identification is calibrated after the strategy. Then, perform NLOS identification first, and then calibrate the NLOS error of the NLOS data identified as NLOS through mitigation, and then use all the data to estimate the position of the target vehicle;

Based on the principle of the smallest probability of interruption in different scenarios, the optimal positioning strategy table in different scenarios is set as shown in Table 2:

Table II

According to the values of the communication range r, _{P NLOS} , _{misrecognition} probability PF and PM corresponding to the vehicle to be tested without GPS, query the above Table 2 to obtain the best positioning strategy.

It can be seen from the technical solutions provided by the above embodiments of the present invention that the present invention proposes a GPS-assisted vehicle positioning algorithm by combining NLOS identification and mitigation technologies, and uses this algorithm to simulate the impact of NLOS propagation on the accuracy of vehicle positioning. , the comparison of vehicle positioning accuracy under different positioning scenarios and positioning strategies is given, and the selection scheme of positioning strategies in different environments is given, which further improves the accuracy of vehicle positioning, and is a vehicle based on precise position information under the background of 5G. Contributed to the realization and promotion of networking applications.

Additional aspects and advantages of the present invention will be set forth in part in the following description, which will be apparent from the following description, or may be learned by practice of the present invention.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

1 is a process flow diagram of a method for a GPS-assisted collaborative vehicle positioning algorithm provided by an embodiment of the present invention;

FIG. 2 shows a positioning interruption probability of “identification only” under three different misrecognition probabilities provided by an embodiment of the present invention.

FIG. 3 shows the positioning interruption probability using "identification first and calibration later" under three different misrecognition probabilities provided by an embodiment of the present invention.

Fig. 4 is provided by an embodiment of the present invention when r=200m, under the same probability of misidentification, different _PFs and _PMs adopt "identification-only" positioning interruption probability.

FIG. 5 shows that when r=200m, under the same misrecognition probability, different _PFs and _PMs adopt the positioning interruption probability of “recognize first and then calibrate” according to an embodiment of the present invention.

FIG. 6 shows that when r=500m, under the same misrecognition probability, different _PFs and _PMs adopt the “recognition-only” positioning interruption probability according to an embodiment of the present invention.

FIG. 7 shows that when r=500m, under the same misrecognition probability, different _PFs and _PMs adopt the positioning interruption probability of “recognize first and then calibrate” according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of a distance measurement according to an embodiment of the present invention.

FIG. 9 is a flowchart of a 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 ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but not to be construed as a limitation of the present invention.

It will be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" as used herein can include the plural forms as well, unless expressly stated otherwise. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude 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 we refer to an element 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. Furthermore, "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 should also be understood that terms such as those defined in the general dictionary should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

In order to facilitate the understanding of the embodiments of the present invention, the following will take several specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

The invention aims to study the relationship between the recognition error of NLOS identification and the positioning accuracy, and improve the vehicle positioning accuracy accordingly. A GPS-assisted co-location algorithm for simulation is proposed, and the recognition errors are divided into two categories: false and miss. The false category refers to the wrong identification of the LOS state as the NLOS state, and the miss category refers to the NLOS status. Status incorrectly identified as LOS status. The method obtains the comparison of localization performance under two different recognition error probabilities when using different localization strategies. On this basis, the positioning strategies with the highest positioning accuracy in different environments are summarized. Taking this as a reference, higher positioning accuracy can be obtained on the premise that the two recognition error probabilities are known.

A processing flow chart of a vehicle positioning method in a GPS-assisted NLOS propagation scenario provided by an embodiment of the present invention is shown in FIG. 1 , the method can improve the vehicle positioning accuracy in the NLOS propagation scenario, and the specific steps are as follows:

Step S1: Generate a simulation model of vehicle positioning.

The embodiment of the present invention provides a GPS-assisted cooperative vehicle positioning algorithm. In this algorithm, each vehicle may be either an anchor point with consistent location information, or a point to be measured whose location is unknown, depending on whether the vehicle has the ability to obtain its own location information through GPS. The algorithm is described in detail here: N _gps is used to represent the number of vehicles with GPS positioning capability, and N _v is used to represent the total number of vehicles. First, the number of vehicles whose own positions are known is denoted as N _know , and initialized as N _know =N _gps . Then iterate. All vehicles are retrieved in each iteration, and if the position of the i-th vehicle is known, no further operations are performed on it. If its position is unknown, first check whether the number of other vehicles with known positions within its communication range (that is, vehicles that can be used as anchor points) is greater than or equal to 3. If so, use the TDOA (time difference of arrival, time difference of arrival) algorithm to calculate Get the position of the i-th vehicle and make N _know =N _know +1; if not, do nothing. Iterate in this way until N _know =N _v , that is, all vehicle positions have been obtained, and the algorithm is stopped.

同时，给出了“仅识别”和“先识别后校准”两种定位策略。其中，“仅识别”策略表示仅使用NLOS identification，对识别为NLOS的信号数据予以丢弃，只利用LOS信号进行定位计算。另外，如果最终识别得到的LOS径的数量少于3，无法进行定位计算，规定此时的定位误差为无穷大；“先识别后校准”则先进行NLOSidentification，再将被识别为NLOS数据经过mitigation校准NLOS误差，之后再把全部数据用于对目标车辆位置的估计。The present invention specifically divides the NLOS recognition error into two categories: false (the LOS state is erroneously recognized as the NLOS state) and miss (the NLOS state is recognized as the LOS state), and the probability of occurrence of the two situations is defined as P _F and P _M. And define the total probability of identification error _PE , and have

At the same time, two positioning strategies of "recognition only" and "calibration after identification" are given. Among them, the "identification only" strategy means that only NLOS identification is used, the signal data identified as NLOS is discarded, and only the LOS signal is used for positioning calculation. In addition, if the number of LOS paths finally identified is less than 3, the positioning calculation cannot be performed, and the positioning error at this time is specified to be infinite; "first identification and then calibration", NLOS identification is performed first, and then the identified NLOS data is subjected to mitigation calibration. NLOS error, and then use all the data to estimate the position of the target vehicle.

Next, the embodiment of the present invention establishes a simulation scene in which the above algorithm is applied to locate the vehicle in the NLOS environment. The simulation scene information and simulation parameters are listed in Table 1.

Table 1 Simulation scene information and simulation parameters

In the simulation, P _NLOS is used to represent the probability of NLOS propagation. The larger the P _NLOS , the more complex the environment where the target is located. The positioning error threshold _eth is given. When the positioning error exceeds _eth , it is considered that positioning interruption occurs. The probability of positioning interruption is denoted by P _out , which is calculated by the Monte Carlo method. In the present invention, P _out is used to represent the accuracy of vehicle positioning.

The simulation of the present invention is divided into three parts. In the first part, we set three groups of [P _F , P _M ], which are [0,0], [0.05, 0.05], [0.1, 0.1] respectively. The simulation parameters are set according to the settings shown in Table 1. The variation curves of P _out with P _NLOS of the three groups of [ _{PF , P M ]} under the two strategies are shown in Figures 2 and 3. In the second part, the parameters are still set according to Table 1, and the values of the three groups of [P _F , P _M ] are set to [0.2, 0.2], [0, 0.4], [0.4, 0] respectively, and the values shown in Figure 4 and Figure 5 plots P _out versus P _NLOS using the "identify only" and "identify before calibration" strategies. In the third part, the maximum communication distance r between vehicles is increased to 500m, and the values of other parameters are still consistent with Table 1. The three groups [P _F , P _M ] are the same as the second part, and the curves of P _out are shown in Fig. 6 and Fig. 7 .

Through the analysis of the simulation results, several conclusions are drawn: (1) The accuracy of NLOS identification will have a relatively large impact on the positioning performance. (2) If the probability of occurrence of NLOS is relatively low, the strategy of "identification only" can better reduce the NLOS error, while the strategy of "identify first and then calibrate" is not as good as "identification only" due to the bias of mitigation; but with the occurrence of NLOS As the probability increases, the number of links in the LOS state less than 3 may increase, and the performance of "identify first and then calibrate" is gradually better than "identify only". (3) When the vehicle communication range r is relatively small, if the strategy of "recognition only" is adopted, the recognition error of false type has a greater impact on the positioning accuracy; if the "recognition first and then calibration" is adopted, the impact of miss is more significant. (4) With the expansion of the vehicle communication range r, the above relationship changes. No matter which strategy is adopted, miss is the most important factor limiting the positioning accuracy.

Finally, combined with the simulation results and the relevant conclusions obtained from the analysis, we summarize the proposed positioning strategy in Table 2. With the help of Table 2, on the premise of measuring _PF and _PM , a positioning strategy that can achieve higher positioning accuracy can be selected.

Table 2 Suggested positioning strategies in different scenarios

Suppose the road is a rectangle whose length is S _l and its width is S _w . A two-dimensional plane coordinate system of the road is established, and N _v non-coincident points are randomly generated within the rectangular range of x∈[0, S _l ], y∈[0, S _w ] to represent N _v vehicles, and the N _v vehicles are numbered from 1 to N _v . Next, N _gps vehicles with GPS are randomly determined among N _v vehicles by the given P _gps . The values of the above parameters are listed in Table 1.

Step S2: Determine the measured distance between vehicles.

The measured distance between the ith vehicle and the jth vehicle can be expressed as:

表示车辆之间的测量距离，m_ij表示测量误差，n_ij表示NLOS误差。由(1)式可知，车辆之间的测量距离由真实距离、测量误差和NLOS传播造成的误差三部分构成。其中，n_ij在LOS环境下为0。如图8所示，测量所有车辆之间的距离，加上测量噪声，生成一个大小为N_v×N_v的矩阵R，其中的元素r_ij表示在LOS传播下测得的第i辆车和第j辆车之间的距离，因此有r_ij＝r_ji和r_ii＝0。在R的基础上加上NLOS误差，得到一个相同大小的矩阵R_NLOS，其表示受NLOS噪声污染后的测量数据。从矩阵R_NLOS中，按照给定的P_NLOS的概率抽取数据作为NLOS径的测量值；从矩阵R中，按照1-P_NLOS的概率抽取LOS径的测量值。in,

represents the measured distance between vehicles, m _ij represents the measurement error, and n _ij represents the NLOS error. It can be seen from equation (1) that the measurement distance between vehicles consists of three parts: the real distance, the measurement error and the error caused by NLOS propagation. Among them, n _ij is 0 in the LOS environment. As shown in Figure 8, the distances between all vehicles are measured, and the measurement noise is added to generate a matrix R of size N _v ×N _v , where the elements r _ij represent the ith vehicle and the ith vehicle measured under LOS propagation. The distance between the jth vehicle, therefore r _ij =r _ji and r _ii =0. The NLOS error is added to R to obtain a matrix R _NLOS of the same size, which represents the measurement data polluted by NLOS noise. From the matrix R _NLOS , extract the data according to the given probability of P _NLOS as the measurement value of NLOS path; from the matrix R, extract the measurement value of LOS path according to the probability of 1-P _NLOS .

Step S3: Locating the vehicle.

FIG. 9 is a flowchart of a vehicle positioning provided by an embodiment of the present invention. As shown in FIG. 9 , the positioning of the vehicle is divided into two situations:

(1) For a vehicle equipped with GPS (or GNSS), its position is directly obtained from GPS. Specifically, the GPS receiver mounted on the vehicle establishes a link with the GPS satellite, and calculates the distance between the two by measuring the RTT (round-triptime, round-trip time) of the signal between the satellite and the receiver. When the GPS receiver measures the distance information between itself and three or more GPS satellites, the position of the vehicle is calculated by the TDOA algorithm and fed back to the vehicle-mounted display device.

(2) For a vehicle without GPS, its position is obtained through the cooperative vehicle positioning algorithm proposed by the present invention. First, the current vehicle establishes communication with all other vehicles within its communication range, and calculates the distance between itself and other vehicles by measuring RTT. At the same time, other vehicles whose positions are known (including vehicles located by GPS and vehicles located by this method) send their own positions to the vehicle to be located currently. Finally, other vehicles with known positions are used as reference points (anchor points), and the distance information between vehicles is used to calculate the position information of the vehicle to be tested through the TDOA algorithm, and feed it back to the on-board display device.

Step S4: Estimate the positioning error.

FIG. 10 is a schematic diagram of a positioning error according to an embodiment of the present invention. As shown in Figure 10, in the present invention, the positioning error refers to the Euclidean distance between the estimated position and the actual position, namely:

为第i辆车的估计位置坐标。需要指出的是，由于本发明关注的是NLOS场景下的定位精度，而非GPS定位的精度，本发明仅统计无GPS的车辆的定位误差。即对于每次定位，计算系统中全部的无GPS的车辆的定位误差，并求取定位误差的平均值e。具体地，e可以用如下方法获得：Among them, e _i represents the positioning error of the i-th vehicle, P _i represents the real position coordinates of the i-th vehicle,

is the estimated position coordinates of the i-th vehicle. It should be noted that, since the present invention focuses on the positioning accuracy in the NLOS scenario rather than the GPS positioning accuracy, the present invention only counts the positioning errors of vehicles without GPS. That is, for each positioning, the positioning errors of all vehicles without GPS in the system are calculated, and the average value e of the positioning errors is obtained. Specifically, e can be obtained as follows:

Among them, I _i is an indicator function, I _i =0 when the i-th vehicle has GPS, otherwise I _i =1.

Step S5: Calculate the outage probability P _out .

Given a maximum allowable positioning error _eth (unit is m), when the distance between the estimated position and the actual position exceeds _eth , it is determined that "interruption" occurs, that is, positioning cannot be achieved, and the probability of interruption is recorded as P _out , and can be obtained by Monte Carlo method, specifically:

是一个指示函数，当

时，

反之

这样在规定的e_th下的中断概率P_out(e_th)可以通过多次实验，并对

取均值

得到。in,

is an indicator function, when

hour,

on the contrary

In this way, the outage probability P _out (e _th ) under the specified _eth can be tested many times, and the

take the mean

get.

Step 6: Choose the best positioning strategy.

By comparison, the positioning strategy adopted when the lowest interruption probability can be achieved in different scenarios is obtained. The specific method is:

(1) Set the vehicle communication range to 200m, and compare the interruption probability under different strategies, that is, compare Figure 3 and Figure 4 horizontally. Assuming P _F > P _M , observe and compare the value of the outage probability P _out (ie, the dotted line marked with a lower triangle) under the two positioning strategies under different P _NLOS , so as to determine when r=200m and P _F > P _M , a strategy that can make P _out as low as possible (that is, the positioning accuracy is as high as possible); similarly, assuming P _F < P _M , compare the interruption probability of the two strategies (that is, the dotted line marked with a five-pointed star), and obtain r The optimal positioning strategy when PF = _200m and _PF < PM.

(2) Set the vehicle communication range to 300m and 500m respectively, and repeat the above steps.

(3) The optimal positioning strategies in various scenarios obtained through the above steps are summarized in the form of Table 2.

It should be pointed out that Table 2 is not fixed here, and will change according to different positioning scenarios and positioning parameters. The present invention only provides an idea and method for improving the positioning accuracy, and the specific application should be adjusted according to the situation.

Step 7: Determine Positioning Parameters

(1) Count the probability of occurrence of NLOS propagation in the current positioning scene, that is, P _NLOS . Two test vehicles with cameras on the roofs are deployed on the test section. If the cameras of the two vehicles can observe each other, the current path is considered to be the LOS path; otherwise, it is the NLOS path. Constantly change the positions of the two vehicles and repeat the above operations to obtain the NLOS/LOS status information of multiple links. P _NLOS can be estimated by Monte Carlo method, namely:

Among them, I _link is an indicator function, which is expressed as:

Take the average value of I _link to get the value of P _NLOS .

(2) Two _{misrecognition} probabilities, ie _PF and PM, are measured. When the P _NLOS measurement in (1) is completed, the LOS/NLOS status information of each link can be obtained at the same time. Use NLOS identification to identify these links, and compare the identification result of the link with the real state. If the real state is LOS, but the identification result is NLOS, a false type error occurs; if the real state is NLOS, but the identification result For LOS, it is miss. After determining the number of false and miss samples, P _F and P _M can be calculated by:

(3) Record the size of the communication range r of the vehicle. r is usually a known parameter and only needs to be recorded before starting the actual positioning.

Step 8: Comparing with the best positioning strategy table in different scenarios, the best positioning strategy is obtained.

Table 2 Suggested positioning strategies in different scenarios

Combining with Table 2, from the size relationship between _PF and _{PM determined in the previous step, the value of r and the size of PNLOS ,} a positioning strategy that is most suitable for the current scene can be determined. This strategy has a relatively low positioning interruption probability. That is, the positioning performance is better. The specific method is: first, according to the communication range of the vehicle, find the corresponding value of r in the table; then, find the corresponding P _NLOS range in the table from the _{P NLOS} measured in step 7; finally, according to the statistics obtained PF and P _M , compare and record the size relationship between the two. From the above three conditions, the positioning strategy with the highest positioning accuracy in the table can be determined. Choosing to use this strategy for determination can further improve the positioning accuracy of the vehicle.

To sum up, the present invention proposes a GPS-assisted vehicle positioning algorithm by combining NLOS identification and mitigation technologies, and uses this algorithm to simulate the influence of NLOS propagation on the accuracy of vehicle positioning, and provides different positioning scenarios and methods. The vehicle positioning accuracy under the positioning strategy is compared, and the results are analyzed. Combined with the simulation results and conclusions, the present invention provides a selection scheme of positioning strategies in different environments, further improves the accuracy of vehicle positioning, and contributes to the realization and promotion of vehicle networking applications based on precise location information in the context of 5G. .

The embodiment of the present invention proposes a method for improving vehicle positioning accuracy in an NLOS propagation environment by combining NLOS identification and mitigation technologies. In this method, the NLOS identification errors are further divided into two categories, and the influence of these two categories on the positioning accuracy is studied in detail. On this basis, the positioning strategies that can achieve better positioning accuracy under different vehicle communication ranges and the probability of NLOS propagation are summarized, and are summarized in a table form for reference. By referring to this table, the positioning accuracy of the vehicle can be further improved with the support of the existing NLOS identification and mitigation technologies, and it has made a certain contribution to the research and promotion of the development of the Internet of Vehicles technology and its application.

Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.

From the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products can be stored in storage media, such as ROM/RAM, magnetic disks, etc. , CD, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The device and system embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, It can be located in one place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (6)

1. a vehicle positioning method under a GPS-assisted NLOS propagation scenario, is characterized in that, comprising: generating a simulation model of vehicle positioning, and using the simulation model to calculate the measurement distance between vehicles; Using the measured distance between vehicles to calculate the estimated position of the vehicle to be tested without GPS through the TDOA algorithm; Calculate the positioning error of the estimated position of the vehicle to be tested without GPS, and calculate the interruption probability according to the positioning error; The best positioning strategy to use when reaching the lowest probability of outage; Use the optimal positioning strategy to position the vehicle to be tested without GPS. 2. The method according to claim 1, wherein, the simulation model for generating vehicle positioning is used to calculate the measurement distance between vehicles by using the simulation model, comprising: Generate a simulation model of vehicle positioning, in the simulation model: use N _gps to represent the number of vehicles with GPS positioning capability, N _v to represent the total number of vehicles, and the number of vehicles whose positions are known is recorded as N _know , and initialized to N _know = N _gps , the vehicle with GPS positioning capability is used as the anchor point for positioning the vehicle without GPS; the NLOS recognition error is divided into two cases: false and miss, the false case means that the LOS state is incorrectly identified as the NLOS state, The miss condition indicates that the NLOS state is identified as the LOS state, the probability of false occurrence is P _F , the probability of miss occurrence is _PM , and the total probability of identification error _PE ,

The calculation method of the measured distance between the i-th vehicle and the j-th vehicle is:

in,

represents the measurement distance between vehicles, m _ij represents the measurement error, n _ij represents the NLOS error, and n _ij is 0 in the LOS environment. 3. method according to claim 2 is characterized in that, described utilizing the measurement distance between vehicles to calculate the estimated position information of the vehicle to be measured without GPS by TDOA algorithm, comprising: For vehicles equipped with on-board GPS, obtain the location information of the vehicle through on-board GPS; For a vehicle to be tested without on-board GPS, the vehicle to be tested establishes communication with all other vehicles within its communication range, and uses other vehicles with known positions as anchor points. The vehicle to be tested calculates the distance between itself and the anchor point by measuring the round-trip time. The anchor point sends its own position information to the vehicle to be tested. The vehicle to be tested uses the distance between itself and the anchor point and the position information of the anchor point to calculate the position information of the vehicle to be tested through the TDOA algorithm, and calculates its position. The information is fed back on the on-board display device. 4. The method according to claim 3, wherein the calculating the positioning error of the estimated position of the vehicle to be measured without GPS, and calculating the interruption probability according to the positioning error, comprising: The Euclidean distance between the estimated position and the actual position of the vehicle to be tested without GPS is the positioning error, and the calculation formula of the positioning error is:

Among them, e _i represents the positioning error of the i-th vehicle, P _i represents the real position coordinates of the i-th vehicle,

is the estimated position coordinates of the i-th vehicle; Find the average value e of the positioning errors e _i of all vehicles to be tested without GPS:

Wherein, I _i is an indicator function, I _i =0 when the i-th vehicle has GPS, otherwise I _i =1; Given a maximum allowable positioning error e _th , when the distance between the estimated position and the actual position exceeds e _th , it is determined that there is an interruption and the positioning cannot be achieved, and the probability of interruption is recorded as P _out , which is calculated by the Monte Carlo method. Outage probability P _out (e _th ) under _eth of :

is an indicator function, when

hour,

on the contrary

5. The method according to claim 4, wherein the determining the positioning parameters of the vehicle to be measured without GPS comprises: Calculate the probability P _NLOS of NLOS propagation in the current positioning scene of the vehicle to be tested without GPS, and deploy two test vehicles with cameras on the roofs in the test section. If the cameras of the two vehicles can observe each other, then It is considered that the current path is the LOS path; otherwise, it is the NLOS path, and the positions of the two vehicles are constantly changed, and the above operations are repeated to obtain the NLOS/LOS state information of multiple links. The P _NLOS is estimated by the Monte Carlo method, that is, :

Among them, I _link is an indicator function, which is expressed as:

Take the mean value of I _link to get the value of P _NLOS ; Use NLOS identification to identify each link, and compare the LOS/NLOS status information identification result of each link with the real status. If the real status is LOS, but the identification result is NLOS, a false type error occurs; The true state is NLOS, but the recognition result is LOS, then it is miss. After determining the number of false and miss samples, the two _{misrecognition} probabilities _PF and PM are calculated by the following formulas:

Record the size of the communication range r of the vehicle to be tested without GPS. 6. The method according to claim 5, wherein the optimal positioning strategy adopted when obtaining the lowest interruption probability under different scenarios according to the positioning parameter by comparison, comprises: Two positioning strategies are set: identification only and first identification and then calibration. The identification only positioning strategy means that only NLOSidentification is used, the signal data identified as NLOS is discarded, and only the LOS signal is used for positioning calculation, and the first identification is calibrated after the strategy. Then, perform NLOS identification first, and then calibrate the NLOS error of the NLOS data identified as NLOS through mitigation, and then use all the data to estimate the position of the target vehicle; Based on the principle of the smallest probability of interruption in different scenarios, the optimal positioning strategy table in different scenarios is set as shown in Table 2: Table II

According to the values of the communication range r, _{P NLOS} , _{misrecognition} probability PF and PM corresponding to the vehicle to be tested without GPS, query the above Table 2 to obtain the best positioning strategy.

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