A vehicle cooperative anomaly detection and localization method, system, device and medium
By using GESD filtering iterative detection and pseudorange differential positioning of GNSS data, the problem of limited accuracy of vehicle cooperative positioning in complex urban environments was solved, achieving efficient and low-cost vehicle self-localization and anomaly detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUN YAT SEN UNIV
- Filing Date
- 2024-01-02
- Publication Date
- 2026-06-05
AI Technical Summary
Existing vehicle cooperative positioning technologies have limited accuracy in complex urban environments, and radio technology has limited sensing range and high cost.
The GESD filtering iterative detection method based on GNSS data is adopted. By using pseudorange differential positioning of common-view satellites and reference satellites, combined with the GNSS pseudorange residual error of adjacent vehicles and the relative pseudorange measurement value, the self-localization and anomaly detection of vehicles are realized.
It improves vehicle positioning accuracy and robustness, dynamically achieves self-positioning, reduces costs, and requires no additional sensor support.
Smart Images
Figure CN117908063B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle positioning technology, and in particular to a method, system, device and medium for vehicle cooperative anomaly detection and positioning. Background Technology
[0002] Currently, research on the vulnerabilities of Global Navigation Satellite Systems (GNSS) is increasing, making the development of a low-cost, simple anomaly detection scheme crucial for ensuring GNSS quality control. With the continuous advancement of vehicle technology, downlink data collection in vehicle-to-everything (V2V) environments has become readily available, facilitating vehicle-to-vehicle (V2V) communication and location information transmission. Cooperative vehicle positioning (CP) is one of the most promising methods to enhance vehicle positioning performance because vehicles can acquire additional sensor data from sensor-rich vehicles without using expensive sensors. Vehicles can interact with all detected vehicle information within a certain range via wireless communication devices. More observational information will help improve the accuracy of vehicle positioning. Compared to traditional single-vehicle GNSS positioning technology, vehicle-mounted CP technology significantly improves positioning accuracy and stability. It offers advantages such as low cost and good system robustness, greatly enhancing the task execution capabilities of multi-vehicle systems.
[0003] However, vehicle-mounted positioning (CP) technology has limited performance in urban areas with dense high-rise buildings and complex environments. Further exploration of positioning algorithms for vehicle-mounted CP technology is needed to improve positioning accuracy and robustness. Previous cooperative methods have employed a combination of radio ranging and GNSS, but radio technology is limited within the sensing range by received signal strength and time-of-arrival (TOA) techniques. Furthermore, these ranging-based CP methods typically require the use of ranging sensors, which imposes an additional burden on the vehicle. Summary of the Invention
[0004] The technical problem this invention aims to solve is that existing vehicle cooperative positioning accuracy is limited by complex urban environments, and radio technology is constrained by received signal strength and time-of-arrival limitations within its sensing range, resulting in high costs. To address these problems, in a first aspect, this invention provides a method for vehicle cooperative anomaly detection and positioning, the method comprising:
[0005] Obtain the GNSS pseudorange residual error of the current vehicle based on the current vehicle's GNSS data;
[0006] The residual error of the GNSS pseudorange of the current vehicle is detected by GESD filtering iteration, and it is determined whether the iteration has converged.
[0007] In response to receiving GNSS data from neighboring vehicles, determine whether the epoch time matches. If they match, select the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle.
[0008] Based on the common-view satellite, the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles is obtained, and based on the reference satellite, the relative pseudorange measurement value of the current vehicle and adjacent vehicles is obtained through pseudorange differential positioning.
[0009] The GESD filter is used to iteratively detect the residual error of the shared GNSS pseudorange and the relative pseudorange measurement value, and it is determined whether the iterations converge. If they converge, the positioning result of the current vehicle is output.
[0010] Furthermore, the step of iteratively detecting the GNSS pseudorange residual error of the current vehicle through GESD filtering and determining whether the iteration has converged also includes:
[0011] If the iteration fails to converge, the GNSS pseudorange residual error of the current vehicle is obtained by pseudorange single-point positioning in the next epoch, and the GNSS pseudorange residual error of the current vehicle is detected iteratively by GESD filtering until the iteration converges.
[0012] Furthermore, the step of iteratively detecting the GNSS pseudorange residual error of the current vehicle through GESD filtering and determining whether the iteration has converged also includes:
[0013] If the iteration is determined to be converged, the current vehicle positioning result will be output if no GNSS data from a neighboring vehicle is received.
[0014] Furthermore, the step of responding to the receipt of GNSS data from neighboring vehicles, determining whether the epoch times match, and selecting the common-view satellite and reference satellite of the current vehicle and neighboring vehicles, also includes:
[0015] If the epoch times of the current vehicle and its neighboring vehicles do not match, the corresponding positioning result of the current vehicle is obtained based on the GNSS data of the current vehicle and output.
[0016] Further, the step of obtaining the shared GNSS pseudorange residual error of the current vehicle and neighboring vehicles based on the shared satellite includes:
[0017] Based on the common-view satellite, obtain the GNSS pseudorange residual error of the current vehicle and adjacent vehicles respectively;
[0018] The shared GNSS pseudorange residual error is obtained by multiplying the GNSS pseudorange residual error of the current vehicle with the GNSS pseudorange residual error of the adjacent vehicle.
[0019] Further, the step of obtaining the relative pseudorange measurement values between the current vehicle and adjacent vehicles through pseudorange differential positioning based on reference satellites includes:
[0020] Based on the reference satellite, pseudorange measurements of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning.
[0021] The relative pseudodistance measurement value is obtained by calculating the difference between the pseudodistance measurement value of the current vehicle and the pseudodistance measurement value of the adjacent vehicle.
[0022] Furthermore, the step of iteratively detecting the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determining whether the iterations converge, and outputting the current vehicle's positioning result if they converge, also includes:
[0023] If there is non-convergence in the iteration of the shared GNSS pseudorange residual error and relative pseudorange measurement value, then obtain the GNSS data of the current vehicle in the next epoch.
[0024] In the next epoch, select the common-view satellite and reference satellite of the current vehicle and neighboring vehicles, and obtain the shared GNSS pseudorange residual error of the current vehicle and neighboring vehicles based on the common-view satellite. Based on the reference satellite, obtain the relative pseudorange measurement value of the current vehicle and neighboring vehicles through pseudorange differential positioning. Then, iteratively detect the shared GNSS pseudorange residual error and the relative pseudorange measurement value through GESD filtering until the iterations converge, and output the positioning result of the current vehicle.
[0025] Secondly, a system for vehicle cooperative anomaly detection and localization, the system comprising:
[0026] The pseudorange residual error acquisition module is used to acquire the GNSS pseudorange residual error of the current vehicle based on the current vehicle's GNSS data.
[0027] The first filtering iteration detection module is used to detect the residual error of the GNSS pseudorange of the current vehicle through GESD filtering iteration, and to determine whether the iteration has converged. If it has converged, the positioning result of the current vehicle is output.
[0028] The matching and judgment module is used to respond to the received GNSS data from neighboring vehicles, determine whether the epoch time matches, and if they match, select the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle.
[0029] The calculation module is used to obtain the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles based on the common-view satellite, and to obtain the relative pseudorange measurement value of the current vehicle and adjacent vehicles through pseudorange differential positioning based on the reference satellite.
[0030] The second filtering iteration detection module is used to iteratively detect the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determine whether the iterations converge. If they converge, the current vehicle positioning result is output.
[0031] Thirdly, the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a vehicle cooperative anomaly detection and localization method as described above.
[0032] Fourthly, the present invention provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a vehicle cooperative anomaly detection and localization method as described above.
[0033] Compared with existing technologies, the vehicle cooperative anomaly detection and localization method, system, device and medium of this invention have the following advantages: by fusing the positions and pseudorange residuals of adjacent vehicles in vehicle localization, vehicle anomaly satellite detection is achieved, thereby dynamically and efficiently realizing self-localization, and the self-movement of each vehicle can also be used to improve cooperation; through the GESD filtering detection and localization method based on GNSS measurement, anomaly detection and elimination of local vehicles and adjacent vehicles are achieved. Attached Figure Description
[0034] Figure 1 This is a flowchart illustrating the novel vehicle cooperative anomaly detection and localization method provided in this embodiment of the invention;
[0035] Figure 2 This is a diagram of a novel vehicle collaborative anomaly detection framework provided in an embodiment of the present invention;
[0036] Figure 3 This is a structural block diagram of the novel vehicle cooperative anomaly detection and localization system provided in the embodiments of the present invention;
[0037] Figure 4 This is a structural diagram of the computer device provided in an embodiment of the present invention. Detailed Implementation
[0038] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0039] It should be noted that the step numbers in this document are only for the convenience of explaining the specific embodiments and are not intended to limit the order in which the steps are executed. The method provided in this embodiment can be executed by a relevant server, and the following description will use a server as the execution subject.
[0040] like Figure 1 As shown, a novel vehicle cooperative anomaly detection and localization method according to a preferred embodiment of the present invention includes steps S1 to S5:
[0041] Step S1: Obtain the residual GNSS pseudorange error of the current vehicle based on the current vehicle's GNSS data;
[0042] The following is a detailed description of the process for obtaining the vehicle's GNSS pseudorange residual error:
[0043] Specifically, a vehicle is selected as the current vehicle. The current vehicle receives GNSS data through a GNSS receiver. Based on the GNSS data, the pseudorange single-point positioning data of the current vehicle is obtained, thereby obtaining the GNSS pseudorange residual error of the current vehicle. This embodiment uses pseudorange single-point positioning, which is fast, convenient to observe, simple to process data, and can be completed independently by a single receiver.
[0044] Step S2: Detect the residual GNSS pseudorange error of the current vehicle through GESD filtering iteration, and determine whether the iteration has converged;
[0045] This embodiment uses a two-stage cooperative localization framework, such as... Figure 2 As shown, the positioning result of the current vehicle is output according to the positioning process of the first filter layer and the second filter layer.
[0046] Specifically, this embodiment uses, as follows Figure 2 As shown in the independent module, a collaborative anomaly detection and localization method based on Generalized Extreme Studentization Deviation Test (GESD) is proposed. The obtained GNSS pseudorange residual error of the current vehicle is filtered and detected by the collaborative anomaly detection method of GESD, and then iterated to determine whether the iteration result converges.
[0047] If the iteration does not converge, the GNSS pseudorange residual error of the current vehicle is obtained by pseudorange single-point positioning in the next epoch, and the GNSS pseudorange residual error of the current vehicle is detected iteratively by GESD filtering until the iteration converges.
[0048] If the iteration is determined to be converged, and no GNSS data from neighboring vehicles is received, the current vehicle positioning result is output after iterative detection through GESD filtering based on the GNSS pseudorange residual error obtained from the current vehicle's GNSS data.
[0049] In addition, by Figure 2 As can be seen from the independent module, adjacent vehicles that are adjacent to the current vehicle but whose GNSS data is not responded to by the current vehicle can achieve independent positioning and filtering anomaly detection under the independent module. This process is the same as the positioning process of the current vehicle mentioned above.
[0050] This embodiment uses the GESD collaborative anomaly detection and localization method to iteratively filter and detect the pseudorange residual error of the current vehicle that has not received GNSS data from neighboring vehicles, thereby performing anomaly detection and elimination in the localization process and achieving accurate localization.
[0051] In steps S3 to S4, in response to receiving GNSS data from neighboring vehicles, it is determined whether the epoch time matches. If they match, the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle are selected.
[0052] Based on the common-view satellite, the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles is obtained, and based on the reference satellite, the relative pseudorange measurement value of the current vehicle and adjacent vehicles is obtained through pseudorange differential positioning.
[0053] The above steps are broken down in detail below:
[0054] Specifically, this embodiment uses, as follows Figure 2 As shown in the connection module, in this embodiment, a vehicle with a good positioning and observation environment is taken as the neighboring vehicle of the current vehicle. The neighboring vehicle runs the positioning process independently in the independent module. First, when the current vehicle responds to receiving the GNSS data of the neighboring vehicle, it determines whether their epoch times match.
[0055] If a match is found, select the shared satellite and reference satellite of the current vehicle and its adjacent vehicles;
[0056] In this embodiment, the shared pseudorange of the current vehicle and the adjacent vehicles are obtained through the common-view satellite, and the corresponding GNSS pseudorange residual error is obtained. The GNSS pseudorange residual error of the current vehicle and the GNSS pseudorange residual error of the adjacent vehicle are multiplied to obtain the shared GNSS pseudorange residual error.
[0057] The GNSS pseudorange residual error of the vehicle is calculated using the following formula:
[0058]
[0059] in, Indicates the pseudorange between the vehicle and the satellite. This indicates the geometric distance between the vehicle's GNSS receiver and the satellite;
[0060] Using the reference satellite, pseudorange measurements of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning; the difference between the pseudorange measurements of the current vehicle and the pseudorange measurements of adjacent vehicles is calculated to obtain the relative pseudorange measurement.
[0061] The pseudodistance measurement value of the vehicle is calculated using the following formula:
[0062]
[0063] Based on the pseudorange measurement value of the vehicle, the relative pseudorange measurement value is calculated using pseudorange differential positioning, and the relative pseudorange measurement value is calculated using the following formula:
[0064]
[0065] in, Indicates residual error;
[0066] If the epoch times of the current vehicle and its neighboring vehicles do not match, the corresponding positioning result of the current vehicle is obtained based on the GNSS data of the current vehicle and output.
[0067] In response to the GNSS pseudorange error shared by the current vehicle and adjacent vehicles and the relative pseudorange measurement values between adjacent vehicles, this embodiment further detects errors in the positioning process by obtaining the pseudorange between the reference satellite and the common-line-of-sight satellite and the vehicle. Furthermore, by introducing GNSS measurements of adjacent vehicles into the consistency detection, the GNSS measurement information shared by the cooperating vehicles can be utilized to not only further reduce the impact of abnormal measurement values such as multipath and non-line-of-sight effects, but also improve detection performance.
[0068] Step S5: Iteratively detect the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determine whether the iterations converge. If they converge, output the current vehicle positioning result.
[0069] The following is a detailed description of the GESD filtering iterative detection process described above:
[0070] Specifically, in this embodiment, the shared GNSS pseudorange residual error and relative pseudorange measurement value are obtained by GESD filtering through iteration, and it is determined whether the iteration is converged. If they are converged, the positioning result of the current vehicle is output.
[0071] If there is non-convergence in the iteration of the shared GNSS pseudorange residual error and relative pseudorange measurement, then the GNSS data of the current vehicle in the next epoch is obtained. In the next epoch, the common-view satellite and reference satellite of the current vehicle and the adjacent vehicle are selected. Based on the common-view satellite, the shared GNSS pseudorange residual error of the current vehicle and the adjacent vehicle is obtained. Based on the reference satellite, the relative pseudorange measurement of the current vehicle and the adjacent vehicle is obtained through pseudorange differential positioning. Then, the shared GNSS pseudorange residual error and the relative pseudorange measurement are iteratively detected by GESD filtering until the iteration converges. Finally, the positioning result of the current vehicle is output.
[0072] Regarding step S5 above, this embodiment of the invention can port the vehicle cooperative positioning method to GNSS positioning software without prior information or adding sensors to the experiment. It is a simple and convenient method to add a GESD filter to the iterative positioning algorithm and has significant potential advantages.
[0073] In summary, this invention provides a novel vehicle cooperative anomaly detection and localization method. It improves positioning accuracy and robustness by utilizing large amounts of data generated by vehicles and strongly correlated error information, establishing a practical and novel two-stage cooperative localization framework. By fusing the positions and pseudorange residuals of adjacent vehicles in the vehicle network, it achieves satellite detection of vehicle anomalies, thereby dynamically and efficiently realizing self-localization. Through the GESD cooperative anomaly detection and localization method, all measurements from local and adjacent vehicles are used for anomaly detection and elimination. By introducing GNSS measurements from adjacent vehicles into the consistency check, it not only improves detection performance but also more effectively isolates multiple anomalies caused by NLOS, multipath, etc. Furthermore, the localization method can be easily ported to GNSS positioning software. This is a simple and convenient method of adding a GESD filter to an iterative positioning algorithm, requiring no prior information or additional sensors for experiments, and has great potential for mass market applications.
[0074] like Figure 3 As shown, this embodiment of the invention also provides a vehicle cooperative anomaly detection and localization system, the system comprising:
[0075] The pseudorange residual error acquisition module S21 is used to acquire the GNSS pseudorange residual error of the current vehicle based on the GNSS data of the current vehicle.
[0076] The first filtering iteration detection module S22 is used to detect the residual error of the GNSS pseudorange of the current vehicle through GESD filtering iteration and to determine whether the iteration has converged.
[0077] The matching judgment module S23 is used to respond to the received GNSS data from neighboring vehicles, determine whether the epoch time matches, and if they match, select the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle.
[0078] The calculation module S24 is used to obtain the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles based on the common-view satellite, and to obtain the relative pseudorange measurement value of the current vehicle and adjacent vehicles through pseudorange differential positioning based on the reference satellite.
[0079] The second filtering iteration detection module S25 is used to iteratively detect the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determine whether the iterations converge. If they converge, the current vehicle positioning result is output.
[0080] The technical features and effects of the vehicle cooperative anomaly detection and localization system proposed in this invention are the same as those of the vehicle cooperative anomaly detection and localization method proposed in this invention, and will not be repeated here. Each module in the above-mentioned vehicle cooperative anomaly detection and localization system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0081] This invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the vehicle cooperative anomaly detection and localization method as described above.
[0082] like Figure 4 As shown in the figure, this invention also provides a computer device. The figure is a structural block diagram of a preferred embodiment of the computer device provided by this invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the vehicle cooperative anomaly detection and localization method as described above.
[0083] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.
[0084] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.
[0085] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.
[0086] It should be noted that the aforementioned computer equipment may include, but is not limited to, processors and memory, as will be understood by those skilled in the art. Figure 4 The block diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components.
[0087] The above description is only a preferred embodiment of the present invention. It should be noted that for ordinary counters in the art, several improvements and substitutions can be made without departing from the counting principle of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.
Claims
1. A method for cooperative anomaly detection and localization of vehicles, characterized in that, The method includes: Obtain the GNSS pseudorange residual error of the current vehicle based on the current vehicle's GNSS data; The residual error of the GNSS pseudorange of the current vehicle is detected by GESD filtering iteration, and it is determined whether the iteration has converged. In response to receiving GNSS data from neighboring vehicles, determine whether the epoch time matches. If they match, select the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle. Based on the common-view satellite, the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles is obtained. Specifically, based on the common-view satellite, the GNSS pseudorange residual error of the current vehicle and adjacent vehicles are obtained separately. The shared GNSS pseudorange residual error is obtained by multiplying the GNSS pseudorange residual error of the current vehicle and the GNSS pseudorange residual error of the adjacent vehicles. The GNSS pseudorange residual error is calculated using the following formula: in, Indicates the pseudorange between the vehicle and the satellite. This indicates the geometric distance between the vehicle's GNSS receiver and the satellite; And based on the reference satellite, the relative pseudorange measurement values of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning; specifically, based on the reference satellite, the pseudorange measurement values of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning, wherein the pseudorange measurement values are calculated using the following formula: The relative pseudodistance measurement value is obtained by calculating the difference between the pseudodistance measurement value of the current vehicle and the pseudodistance measurement values of adjacent vehicles. The relative pseudodistance measurement value is calculated using the following formula: in, Indicates residual error; The GESD filter is used to iteratively detect the residual error of the shared GNSS pseudorange and the relative pseudorange measurement value, and it is determined whether the iterations converge. If they converge, the positioning result of the current vehicle is output.
2. The vehicle cooperative anomaly detection and localization method according to claim 1, characterized in that, The step of iteratively detecting the residual GNSS pseudorange error of the current vehicle through GESD filtering and determining whether the iteration has converged also includes: If the iteration fails to converge, the GNSS pseudorange residual error of the current vehicle is obtained by pseudorange single-point positioning in the next epoch, and the GNSS pseudorange residual error of the current vehicle is detected iteratively by GESD filtering until the iteration converges.
3. The vehicle cooperative anomaly detection and localization method according to claim 2, characterized in that, The step of iteratively detecting the residual GNSS pseudorange error of the current vehicle through GESD filtering and determining whether the iteration has converged also includes: If the iteration is determined to be converged, the current vehicle positioning result will be output if no GNSS data from a neighboring vehicle is received.
4. The vehicle cooperative anomaly detection and localization method according to claim 1, characterized in that, The response to receiving GNSS data from neighboring vehicles, determining whether the epoch times match, and if they match, selecting the common-view satellite and reference satellite for the current vehicle and neighboring vehicles, also includes: If the epoch times of the current vehicle and its neighboring vehicles do not match, the corresponding positioning result of the current vehicle is obtained based on the GNSS data of the current vehicle and output.
5. The vehicle cooperative anomaly detection and localization method according to claim 1, characterized in that, The step of iteratively detecting the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determining whether the iterations converge, and outputting the current vehicle positioning result if they converge, also includes: If there is non-convergence in the iteration of the shared GNSS pseudorange residual error and relative pseudorange measurement value, then obtain the GNSS data of the current vehicle in the next epoch. In the next epoch, select the common-view satellite and reference satellite of the current vehicle and its neighboring vehicles, and obtain the shared GNSS pseudorange residual error of the current vehicle and its neighboring vehicles based on the common-view satellite. Based on the reference satellite, obtain the relative pseudorange measurement value of the current vehicle and its neighboring vehicles through pseudorange differential positioning. Then, iteratively detect the shared GNSS pseudorange residual error and the relative pseudorange measurement value through GESD filtering until the iterations converge, and output the positioning result of the current vehicle.
6. A system for cooperative anomaly detection and localization of vehicles, characterized in that, The system includes: The pseudorange residual error acquisition module is used to acquire the GNSS pseudorange residual error of the current vehicle based on the current vehicle's GNSS data. The first filtering iteration detection module is used to detect the residual GNSS pseudorange error of the current vehicle through GESD filtering iteration and to determine whether the iteration has converged. The matching and judgment module is used to respond to the received GNSS data from neighboring vehicles, determine whether the epoch time matches, and if they match, select the common-view satellite and reference satellite of the current vehicle and the neighboring vehicle. The calculation module is used to obtain the shared GNSS pseudorange residual error of the current vehicle and adjacent vehicles based on the common-view satellites. Specifically, it obtains the GNSS pseudorange residual error of the current vehicle and adjacent vehicles respectively based on the common-view satellites; it then multiplies the GNSS pseudorange residual error of the current vehicle and the GNSS pseudorange residual error of the adjacent vehicles to obtain the shared GNSS pseudorange residual error. The GNSS pseudorange residual error is calculated using the following formula: in, Indicates the pseudorange between the vehicle and the satellite. This indicates the geometric distance between the vehicle's GNSS receiver and the satellite; And based on the reference satellite, the relative pseudorange measurement values of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning; specifically, based on the reference satellite, the pseudorange measurement values of the current vehicle and adjacent vehicles are obtained through pseudorange differential positioning, wherein the pseudorange measurement values are calculated using the following formula: The relative pseudodistance measurement value is obtained by calculating the difference between the pseudodistance measurement value of the current vehicle and the pseudodistance measurement values of adjacent vehicles. The relative pseudodistance measurement value is calculated using the following formula: in, Indicates residual error; The second filtering iteration detection module is used to iteratively detect the shared GNSS pseudorange residual error and relative pseudorange measurement value through GESD filtering, and determine whether the iterations converge. If they converge, the current vehicle positioning result is output.
7. A computer device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the vehicle cooperative anomaly detection and localization method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the vehicle cooperative anomaly detection and localization method as described in any one of claims 1 to 5.