CN117908063A - Method, system, equipment and medium for detecting and positioning cooperative abnormality of vehicle - Google Patents
Method, system, equipment and medium for detecting and positioning cooperative abnormality of vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of vehicle positioning, and discloses a method, a system, equipment and a medium for detecting and positioning cooperative abnormality of a vehicle. Acquiring GNSS pseudo-range residual errors of the current vehicle according to the GNSS data of the current vehicle; detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iteration, and judging whether the iteration converges or not; responding to received GNSS data of the adjacent vehicles, and if epoch time is matched, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicles; obtaining a shared GNSS pseudo-range residual error of a current vehicle and an adjacent vehicle according to the common-view satellite, and obtaining a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to a reference satellite; detecting residual errors of the shared GNSS pseudo range and relative pseudo range measurement values through GESD filtering respectively in an iterative mode, judging whether the iterations are converged or not, and outputting a positioning result of the current vehicle if the iterations are converged; multiple anomalies can be more effectively isolated, thereby dynamically and efficiently achieving self-localization.
Description
Technical Field
The invention relates to the technical field of vehicle positioning, in particular to a method, a system, equipment and a medium for detecting and positioning cooperative abnormality of a vehicle.
Background
Currently, research on vulnerability of Global Navigation Satellite Systems (GNSS) is increasing, and it is important to develop an anomaly detection scheme with low cost and simple algorithm to ensure quality control of the global navigation satellite system. With continued advances in vehicle technology, downlink data collection in a vehicle networking environment has become plagued and it has become easy to implement vehicle-to-vehicle (V2V) communications and transmit location information. Vehicle co-location (CP) is one of the most promising approaches to enhance vehicle positioning performance because the vehicle can acquire additional sensor data from a sensor-rich vehicle without the use of expensive sensors. The vehicle may interact with all vehicle information detected within a certain range via the wireless communication device. More observation information will help to improve the accuracy of vehicle positioning. Compared with the traditional bicycle GNSS positioning technology, the positioning precision and stability of the vehicle-mounted CP technology are improved obviously. The method has the advantages of low cost, good system robustness and the like, and the task execution capacity of the multi-vehicle system is greatly improved.
However, the in-vehicle CP technology has a limit in urban areas where high-rise buildings are dense and environments are complex. Further exploration of positioning algorithms of the vehicle-mounted CP technology is needed to improve positioning accuracy and robustness. Previous cooperative methods employ a combination of radio ranging and GNSS, but radio technology is limited in the perceived range by the received signal strength and time-of-arrival techniques. In addition, these ranging-based CP methods generally require the use of ranging sensors, which can impose an additional burden on the vehicle.
Disclosure of Invention
The invention aims to solve the technical problems that the existing vehicle co-location precision is limited by a complex urban environment, the radio technology is limited by the received signal strength and arrival time technology in a perception range, and the cost is high, and in order to solve the technical problems, in a first aspect, the invention provides a vehicle co-location anomaly detection and location method, which comprises the following steps:
Acquiring GNSS pseudo-range residual errors of the current vehicle according to the GNSS data of the current vehicle;
Detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iteration, and judging whether the iteration converges or not;
In response to receiving GNSS data of the adjacent vehicle, judging whether epoch time is matched or not, and if so, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle;
obtaining a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and obtaining a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite;
And carrying out iterative detection on the residual error of the shared GNSS pseudo range and the relative pseudo range measured value respectively through GESD filtering, judging whether the iterations are converged or not, and outputting the positioning result of the current vehicle if the iterations are converged.
Further, the iterative detection of the GNSS pseudo-range residual error of the current vehicle through GESD filtering, and determining whether the iteration converges, further includes:
If the iteration is judged not to be converged, acquiring the GNSS pseudo-range residual error of the current vehicle through pseudo-range single-point positioning in the next epoch, and iteratively detecting the GNSS pseudo-range residual error of the current vehicle through GESD filtering until the iteration is converged.
Further, the iterative detection of the GNSS pseudo-range residual error of the current vehicle through GESD filtering, and determining whether the iteration converges, further includes:
and if the iteration is judged to be converged, outputting a current vehicle positioning result when the GNSS data of the adjacent vehicle is not received in response.
Further, in response to receiving GNSS data of the neighboring vehicle, determining whether epoch times match, and if so, selecting a common view satellite and a reference satellite of the current vehicle and the neighboring vehicle: further comprises:
If the epoch time of the current vehicle and the epoch time of the adjacent vehicle are not matched, the corresponding positioning result of the current vehicle is obtained according to the GNSS data of the current vehicle and is output.
Further, the obtaining, according to the common view satellite, a shared GNSS pseudo-range residual error of the current vehicle and the neighboring vehicle includes:
According to the common-view satellite, GNSS pseudo-range residual errors of the current vehicle and the adjacent vehicle are respectively obtained;
And performing product operation on the GNSS pseudo-range residual error of the current vehicle and the GNSS pseudo-range residual error of the adjacent vehicle to obtain the shared GNSS pseudo-range residual error.
Further, the obtaining the relative pseudo-range measurement value of the current vehicle and the adjacent vehicle according to the reference satellite through pseudo-range differential positioning comprises the following steps:
According to the reference satellite, pseudo-range measurement values of the current vehicle and the adjacent vehicle are obtained through pseudo-range differential positioning respectively;
and carrying out difference value operation on the pseudo-range measured value of the current vehicle and the pseudo-range measured value of the adjacent vehicle to obtain the relative pseudo-range measured value.
Further, the steps of iteratively detecting the residual error of the shared GNSS pseudo-range and the measurement value of the relative pseudo-range through GESD filtering, judging whether the iterations are converged, and if so, outputting the positioning result of the current vehicle, and further include:
If the iteration of the shared GNSS pseudo-range residual error and the relative pseudo-range measured value is not converged, acquiring GNSS data of the current vehicle in the next epoch;
Selecting a common-view satellite and a reference satellite of a current vehicle and an adjacent vehicle in the next epoch, acquiring a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and acquiring relative pseudo-range measurement values of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite; and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measured value respectively through GESD filtering until the iterations are converged, and outputting the positioning result of the current vehicle.
In a second aspect, a system for collaborative anomaly detection and localization for a vehicle, the system comprising:
the pseudo-range residual error acquisition module is used for acquiring GNSS pseudo-range residual errors of the current vehicle according to the GNSS data of the current vehicle;
The first filtering iteration detection module is used for detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iterations, judging whether the iterations are converged, and outputting a positioning result of the current vehicle if the iterations are converged;
The matching judging module is used for responding to the received GNSS data of the adjacent vehicles, judging whether epoch time is matched or not, and selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicles if the epoch time is matched;
The calculation module is used for acquiring a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and acquiring a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite;
and the second filtering iteration detection module is used for respectively and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measurement value through GESD filtering, judging whether the iteration is converged or not, and outputting the positioning result of the current vehicle if the iteration is converged.
In a third aspect, the present invention provides a computer device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing a vehicle collaborative anomaly detection and localization method as described above when the computer program is executed.
In a fourth aspect, the present invention provides a computer-readable storage medium comprising a stored computer program; wherein the computer program, when run, controls a device in which the computer readable storage medium resides to perform a vehicle collaborative anomaly detection and localization method as described above.
Compared with the prior art, the vehicle collaborative anomaly detection and positioning method, system, equipment and medium have the beneficial effects that: by fusing the positions of adjacent vehicles in vehicle positioning and pseudo-range residuals, abnormal satellite detection of the vehicles is realized, so that self-positioning is dynamically and efficiently realized, and self-movement of each vehicle can be used for improving cooperation; anomaly detection and elimination of the local vehicle and the adjacent vehicle are realized through GESD filtering detection and positioning methods based on GNSS measurement.
Drawings
FIG. 1 is a schematic flow chart of a novel vehicle collaborative anomaly detection and localization method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a novel vehicle collaborative anomaly detection framework provided by an embodiment of the present invention;
FIG. 3 is a block diagram of a novel vehicle collaborative anomaly detection and localization system provided by an embodiment of the present invention;
fig. 4 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
It should be noted that, the step numbers herein are only for convenience of explanation of the specific embodiments, and are not used as limiting the order of execution of the steps. The method provided in this embodiment may be executed by a relevant server, and the following description will take the server as an execution body as an example.
As shown in fig. 1, a novel vehicle collaborative anomaly detection and positioning method according to a preferred embodiment of the present invention includes steps S1 to S5:
step S1, acquiring GNSS pseudo-range residual errors of a current vehicle according to GNSS data of the current vehicle;
The following details the acquisition process of the GNSS pseudo-range residual error of the vehicle:
Specifically, a vehicle is selected as a current vehicle, the current vehicle receives GNSS data of the current vehicle through a GNSS receiver, and according to the GNSS data, pseudo-range single-point positioning data of the current vehicle are obtained so as to obtain the residual error of the pseudo-range of the current vehicle.
Step S2, detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iteration, and judging whether the iteration converges or not;
in this embodiment, as shown in fig. 2, the positioning result of the current vehicle is output according to the positioning process of the first filtering layer and the second filtering layer through the two-stage co-positioning framework.
Specifically, in this embodiment, as shown in the independent module of fig. 2, a collaborative anomaly detection and positioning method based on a generalized extreme chemical and biochemical deviation test, that is, GESD is provided, filtering detection is performed on the obtained GNSS pseudo-range residual error of the current vehicle by using the collaborative anomaly detection method of GESD, and iteration is performed, so as to determine whether the iteration result is converged;
If iteration is not converged, acquiring GNSS pseudo-range residual errors of the current vehicle through pseudo-range single-point positioning in the next epoch, and iteratively detecting the GNSS pseudo-range residual errors of the current vehicle through GESD filtering until iteration is converged;
If iteration convergence is judged, when GNSS data of the adjacent vehicle is not received in response, outputting a current vehicle positioning result after carrying out filtering iteration detection through GESD according to a GNSS pseudo-range residual error acquired by the GNSS data of the current vehicle;
In addition, as can be seen from the independent module of fig. 2, the adjacent vehicles adjacent to the current vehicle but not responded to the GNSS data by the current vehicle realize independent positioning and filtering anomaly detection under the independent module, and the process is the same as the positioning process method of the current vehicle.
According to the embodiment, iterative filtering detection is carried out on the pseudo-range residual errors of the current vehicle which does not receive the GNSS data of the adjacent vehicle through the GESD collaborative anomaly detection and positioning method, and anomaly detection and elimination in the positioning process are carried out, so that accurate positioning can be achieved.
S3 to S4, judging whether epoch time is matched or not in response to receiving GNSS data of the adjacent vehicle, and if so, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle;
Obtaining a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and obtaining a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to a reference satellite;
The following refines the procedure of the steps:
Specifically, in this embodiment, as shown in the connection module of fig. 2, a vehicle with a good positioning and observing environment is used as a neighboring vehicle of the current vehicle, and the neighboring vehicle independently operates the positioning process when the neighboring vehicle is in the independent module, and firstly, when the current vehicle responds to the GNSS data of the neighboring vehicle, it is determined whether the epoch time of the current vehicle is matched;
If the current vehicle is matched with the adjacent vehicle, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle;
The embodiment respectively acquires the common pseudo-range of the current vehicle and the adjacent vehicle and acquires the corresponding GNSS pseudo-range residual error through the common-view satellite; performing product operation on the GNSS pseudo-range residual error of the current vehicle and the GNSS pseudo-range residual error of the adjacent vehicle to obtain the shared GNSS pseudo-range residual error;
The GNSS pseudo-range residual error of the vehicle is calculated using the following formula:
wherein, Representing pseudoranges between vehicle and satellite,/>Representing a geometric distance between the vehicle GNSS receiver and the satellite;
Obtaining pseudo-range measurement values of a current vehicle and an adjacent vehicle through pseudo-range differential positioning through the reference satellite respectively; performing difference value operation on the pseudo-range measured value of the current vehicle and the pseudo-range measured value of the adjacent vehicle to obtain the relative pseudo-range measured value;
The pseudorange measurements for the vehicle are calculated using the following formula:
calculating the relative pseudo-range measurement value by using pseudo-range differential positioning according to the pseudo-range measurement value of the vehicle, and calculating the relative pseudo-range measurement value by adopting the following formula:
wherein, Representing error residuals;
If the epoch time of the current vehicle and the epoch time of the adjacent vehicle are not matched, acquiring and outputting a positioning result of the corresponding current vehicle according to the GNSS data of the current vehicle;
For the GNSS pseudo-range error shared by the current vehicle and the adjacent vehicle and the relative pseudo-range measured value between the adjacent vehicle, the embodiment further detects the error in the positioning process by acquiring pseudo-ranges between the reference satellite and the co-view satellite and the vehicle, and introduces GNSS measurement of the adjacent vehicle in consistency detection, thereby utilizing the GNSS measurement information shared by the cooperative vehicles, not only further reducing the influence of abnormal measured values such as multipath and non-line-of-sight effect, but also improving the detection performance.
And S5, respectively and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measurement value through GESD filtering, judging whether the iterations are converged or not, and outputting the positioning result of the current vehicle if the iterations are converged.
The following details the above GESD iterative filter detection process:
specifically, in this embodiment, through GESD filtering, the residual error of the shared GNSS pseudo-range and the relative pseudo-range measurement value obtained by iterative detection are respectively determined, whether the iterations are all converged is determined, and if so, the positioning result of the current vehicle is output;
If the iteration of the shared GNSS pseudo-range residual error and the relative pseudo-range measured value is not converged, acquiring GNSS data of the current vehicle in a next epoch, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle in the next epoch, acquiring the shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and acquiring the relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite; and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measured value respectively through GESD filtering until the iterations are converged, and outputting the positioning result of the current vehicle.
Aiming at the step S5, the embodiment of the invention can be used for transplanting the vehicle co-location method into GNSS location software without prior information or adding a sensor for experiments, is a simple and convenient method for adding GESD filters in an iterative location algorithm, and has full prospect.
In summary, the embodiment of the invention provides a novel vehicle collaborative anomaly detection and positioning method, which improves positioning accuracy and robustness by utilizing big data and strong related error information generated by a vehicle, and establishes a practical and novel two-stage collaborative positioning frame; by fusing the positions of adjacent vehicles in the vehicle network and pseudo-range residuals, abnormal satellite detection of the vehicles is realized, so that self-positioning is dynamically and efficiently realized; all measured values of the local vehicle and the adjacent vehicle are used for abnormality detection and elimination through a GESD cooperative abnormality detection and positioning method; by introducing GNSS measurements of neighboring vehicles in the consistency check, not only can the detection performance be improved, but also multiple anomalies caused by NLOS, multipath, etc. can be more effectively isolated. In addition, the positioning method can be easily transplanted into GNSS positioning software, is a simple and convenient method for adding GESD filters in an iterative positioning algorithm, does not need to know information in advance or add sensors for experiments, and has great potential in mass market application.
As shown in fig. 3, the embodiment of the invention further provides a system for detecting and locating a cooperative abnormality of a vehicle, which comprises:
The pseudo-range residual error acquisition module S21 is configured to acquire a GNSS pseudo-range residual error of the current vehicle according to GNSS data of the current vehicle;
The first filtering iteration detection module S22 is configured to detect a GNSS pseudo-range residual error of the current vehicle through GESD filtering iterations, and determine whether the iterations converge;
the matching judging module S23 is configured to judge whether epoch time is matched in response to receiving GNSS data of an adjacent vehicle, and if so, select a common view satellite and a reference satellite of the current vehicle and the adjacent vehicle;
the calculating module S24 is configured to obtain a shared GNSS pseudo-range residual error of the current vehicle and the neighboring vehicle according to the common-view satellite, and obtain a relative pseudo-range measurement value of the current vehicle and the neighboring vehicle through pseudo-range differential positioning according to the reference satellite;
And the second filtering iteration detection module S25 is used for respectively and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measurement value through GESD filtering, judging whether the iteration is converged or not, and outputting the positioning result of the current vehicle if the iteration is converged.
The technical features and technical effects of the vehicle collaborative anomaly detection and positioning system provided by the embodiment of the invention are the same as those of the vehicle collaborative anomaly detection and positioning method provided by the embodiment of the invention, and are not repeated here. The various modules in the vehicle collaborative anomaly detection and localization system described above may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program; wherein the computer program, when run, controls a device in which the computer-readable storage medium resides to perform the vehicle collaborative anomaly detection and localization method as described above.
As shown in fig. 4, an embodiment of the present invention further provides a computer device, which is a block diagram of a preferred embodiment of the computer device provided by the present invention, and the computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the method for detecting and locating a collaborative anomaly of a vehicle as described above when the computer program is executed.
Preferably, the computer program may be divided into one or more modules/units (e.g. computer program 1, computer program 2, … …) stored in the memory and executed by the processor to complete the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf programmable gate array (field-programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc., or the processor may be a microprocessor, or the processor may be any conventional processor that is a control center of the computer device, connecting the various parts of the computer device using various interfaces and lines.
The memory mainly includes a program storage area, which may store an operating system, an application program required for at least one function, and the like, and a data storage area, which may store related data and the like. In addition, the memory may be a high-speed random access memory, a nonvolatile memory such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like, or the memory may be other volatile solid-state memory devices.
It should be noted that the above-mentioned computer device may include, but is not limited to, a processor, a memory, and those skilled in the art will appreciate that the structural block diagram of fig. 4 is merely an example of a computer device, and does not constitute a limitation of a computer device, and may include more or less components than illustrated, or may combine some components, or different components.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and substitutions will now occur to those skilled in the art without departing from the spirit of the present invention, and these modifications and substitutions should also be considered to be within the scope of the present invention.
Claims (10)
1. A method for detecting and locating a cooperative abnormality of a vehicle, the method comprising:
Acquiring GNSS pseudo-range residual errors of the current vehicle according to the GNSS data of the current vehicle;
Detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iteration, and judging whether the iteration converges or not;
In response to receiving GNSS data of the adjacent vehicle, judging whether epoch time is matched or not, and if so, selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle;
obtaining a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and obtaining a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite;
And carrying out iterative detection on the residual error of the shared GNSS pseudo range and the relative pseudo range measured value respectively through GESD filtering, judging whether the iterations are converged or not, and outputting the positioning result of the current vehicle if the iterations are converged.
2. The method for detecting and locating cooperative anomalies of a vehicle according to claim 1, wherein the iterative detection of GNSS pseudo-range residual errors of a current vehicle by GESD filters and determining if the iterations converge, further comprises:
If the iteration is judged not to be converged, acquiring the GNSS pseudo-range residual error of the current vehicle through pseudo-range single-point positioning in the next epoch, and iteratively detecting the GNSS pseudo-range residual error of the current vehicle through GESD filtering until the iteration is converged.
3. The method for detecting and locating cooperative anomalies of a vehicle according to claim 2, wherein the iterative detection of GNSS pseudo-range residual errors of a current vehicle by GESD filters and determining if the iterations converge, further comprises:
and if the iteration is judged to be converged, outputting a current vehicle positioning result when the GNSS data of the adjacent vehicle is not received in response.
4. The method for detecting and locating cooperative abnormality of vehicles according to claim 1, wherein in response to receiving GNSS data of a neighboring vehicle, determining whether epoch times match, if so, selecting a common view satellite and a reference satellite of the current vehicle and the neighboring vehicle: further comprises:
If the epoch time of the current vehicle and the epoch time of the adjacent vehicle are not matched, the corresponding positioning result of the current vehicle is obtained according to the GNSS data of the current vehicle and is output.
5. The method for detecting and locating a cooperative anomaly of a vehicle according to claim 1, wherein the obtaining a shared GNSS pseudo-range residual error of a current vehicle and a neighboring vehicle from the common view satellite comprises:
According to the common-view satellite, GNSS pseudo-range residual errors of the current vehicle and the adjacent vehicle are respectively obtained;
And performing product operation on the GNSS pseudo-range residual error of the current vehicle and the GNSS pseudo-range residual error of the adjacent vehicle to obtain the shared GNSS pseudo-range residual error.
6. The method for detecting and locating a cooperative abnormality of a vehicle according to claim 1, wherein said obtaining relative pseudo-range measurements of a current vehicle and an adjacent vehicle from said reference satellite by pseudo-range differential positioning comprises:
According to the reference satellite, pseudo-range measurement values of the current vehicle and the adjacent vehicle are obtained through pseudo-range differential positioning respectively;
and carrying out difference value operation on the pseudo-range measured value of the current vehicle and the pseudo-range measured value of the adjacent vehicle to obtain the relative pseudo-range measured value.
7. The method for detecting and locating a cooperative anomaly of a vehicle according to claim 1, wherein the steps of iteratively detecting the residual error of the shared GNSS pseudo-range and the relative pseudo-range measurement value, respectively, by GESD filtering, and determining whether the iterations are converged, and if so, outputting a locating result of the current vehicle, further comprises:
If the iteration of the shared GNSS pseudo-range residual error and the relative pseudo-range measured value is not converged, acquiring GNSS data of the current vehicle in the next epoch;
Selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicle in the next epoch, acquiring a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and acquiring a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite; and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measured value respectively through GESD filtering until the iterations are converged, and outputting the positioning result of the current vehicle.
8. A system for collaborative anomaly detection and localization of a vehicle, the system comprising:
the pseudo-range residual error acquisition module is used for acquiring GNSS pseudo-range residual errors of the current vehicle according to the GNSS data of the current vehicle;
The first filtering iteration detection module is used for detecting GNSS pseudo-range residual errors of the current vehicle through GESD filtering iterations and judging whether the iterations are converged or not;
The matching judging module is used for responding to the received GNSS data of the adjacent vehicles, judging whether epoch time is matched or not, and selecting a common-view satellite and a reference satellite of the current vehicle and the adjacent vehicles if the epoch time is matched;
The calculation module is used for acquiring a shared GNSS pseudo-range residual error of the current vehicle and the adjacent vehicle according to the common-view satellite, and acquiring a relative pseudo-range measured value of the current vehicle and the adjacent vehicle through pseudo-range differential positioning according to the reference satellite;
and the second filtering iteration detection module is used for respectively and iteratively detecting the residual error of the shared GNSS pseudo range and the relative pseudo range measurement value through GESD filtering, judging whether the iteration is converged or not, and outputting the positioning result of the current vehicle if the iteration is converged.
9. A computer device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the vehicle collaborative anomaly detection and localization method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, wherein the computer readable storage medium comprises a stored computer program; wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the vehicle collaborative anomaly detection and localization method according to any one of claims 1 to 7.
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