Vehicle positioning anomaly detection method, device, equipment, medium and program

Abstract

The application relates to the technical field of vehicles, in particular to a vehicle positioning anomaly detection method, device, equipment, medium and program, wherein the method comprises the following steps: acquiring image data in a vehicle driving process; analyzing vehicle motion parameters in a three-dimensional coordinate system with a vehicle body center point as the center according to the image data; comparing the motion parameters with corresponding parameters output in real time by a vehicle sensor to determine whether the vehicle positioning is abnormal; and if the vehicle positioning is abnormal, controlling the vehicle to switch from a current positioning mode to a target positioning mode. Therefore, the problems in the prior art that the vehicle positioning is prone to being affected by factors such as signals and leads to inaccurate positioning, and after the positioning anomaly is found, the anomaly root cause is mostly confirmed by manual comparison of a map matching track, which leads to low work efficiency, poor real-time performance, poor user experience and the like are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, device, equipment, medium, and program for detecting vehicle positioning anomalies. Background Technology

[0002] Currently, there are two main navigation and positioning methods used in vehicles. The first relies solely on GPS positioning signals: determining location through latitude and longitude information. However, GPS positioning signals are affected by natural factors such as buildings, resulting in poor signal quality and inaccurate positioning. The second method uses GPS information as an auxiliary tool, prioritizing dead reckoning using vehicle sensors and merging the parameter information transmitted by the vehicle body. The main parameters used include the changes in the x, y, and z axes of gyroscope and accelerometer sensors, vehicle speed information, and the vehicle's turning direction determined by the gyroscope deflection angle, as well as the vehicle's speed determined by changes in vehicle speed. However, if the vehicle body parameters are inaccurate, it will trigger extremely inaccurate navigation and positioning, inevitably causing irreparable damage.

[0003] Regarding the handling of positioning deviation issues, after detecting positioning anomalies, data is uploaded to the cloud, and the vehicle's logs are subsequently retrieved remotely and manually analyzed for confirmation. However, it is impossible to determine whether the deviation is caused by human error or abnormal vehicle parameters, resulting in untimely problem handling. Additionally, there are instances of missed or incorrect diagnoses, leading to low work efficiency, poor real-time performance, and a poor user experience. Summary of the Invention

[0004] This application provides a method, device, equipment, medium, and program for detecting vehicle positioning anomalies, in order to solve the problems in related technologies where vehicle positioning is easily affected by factors such as signals, leading to inaccurate positioning. Moreover, after detecting positioning anomalies, most of the time, manual comparison of map matching trajectories is used to confirm the root cause of the anomaly, resulting in low work efficiency, poor real-time performance, and poor user experience.

[0005] The first aspect of this application provides a method for detecting vehicle positioning anomalies, comprising the following steps: acquiring image data during vehicle operation; analyzing vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the image data; comparing the motion parameters with corresponding parameters output in real time by vehicle sensors to determine whether the vehicle positioning is abnormal; and if the vehicle positioning is abnormal, controlling the vehicle to switch from the current positioning mode to the target positioning mode.

[0006] Optionally, the step of parsing the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the image data includes: extracting key features from the image data; and identifying the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the key features.

[0007] Optionally, identifying vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point based on the key features includes: identifying the first motion change amount, the second motion change amount, and the third motion change amount in the third target axis direction of the vehicle in the key features; and generating vehicle motion parameters by calculating the angle change amount and acceleration in different dimensions based on the first motion change amount to the third motion change amount using a motion trajectory tracking algorithm.

[0008] Optionally, the motion parameters include the pitch angle change and pitch acceleration about the first target axis, the roll angle change and roll acceleration about the second target axis, and the yaw angle change and yaw acceleration about the third target axis.

[0009] Optionally, the step of comparing the motion parameters with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal includes: if the deviation between the motion parameters and the corresponding parameters output in real time by the vehicle sensors is lower than a preset threshold, then the vehicle positioning is determined to be normal; otherwise, the vehicle positioning is determined to be abnormal.

[0010] Optionally, it also includes: continuously monitoring the deviation between the vehicle body sensor data and the image analysis results, and automatically switching back to the current positioning mode when the deviation value returns to within a preset threshold.

[0011] A second aspect of this application provides a vehicle positioning anomaly detection device, comprising the following steps: an acquisition module for acquiring image data during vehicle operation; an analysis module for analyzing vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the image data; a comparison module for comparing the motion parameters with corresponding parameters output in real time by vehicle sensors to determine whether the vehicle positioning is abnormal; and a control module for controlling the vehicle to switch from the current positioning mode to the target positioning mode if the vehicle positioning is abnormal.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the vehicle positioning anomaly detection method as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the vehicle positioning anomaly detection method as described in the above embodiments.

[0014] A fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the vehicle positioning anomaly detection method as described in the above embodiments.

[0015] Therefore, this application has at least the following beneficial effects:

[0016] This application embodiment can use a camera to capture images of the vehicle in real time during its driving process. Based on the processed images, the vehicle's motion parameters in a three-dimensional coordinate system are analyzed, including the changing angles and accelerations around the x, y, and z axes. The vehicle motion parameters obtained from the images are compared with the data output by the vehicle sensors. If there is a significant difference between the two, it indicates that the vehicle may have a positioning anomaly. Once a positioning anomaly is confirmed, the system will automatically stop using the current fusion positioning method to avoid navigation deviations caused by incorrect vehicle parameters. This effectively improves the detection capability and efficiency of positioning anomalies during real-time vehicle navigation, ensuring the real-time, accuracy, and reliability of positioning information. When a positioning anomaly is detected, timely measures are taken to reduce the potential risks caused by delayed corrections and improve the user experience.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0019] Figure 1 This is a flowchart of a vehicle positioning anomaly detection method provided according to an embodiment of this application;

[0020] Figure 2 This is a schematic diagram of the x, y, and z axes of a vehicle body gyroscope provided according to an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of the image acquisition and analysis process provided according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram illustrating the positioning method switching logic provided in the embodiments of this application;

[0023] Figure 5 This is a block diagram of a vehicle positioning anomaly detection device provided according to an embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote 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 intended to explain this application, and should not be construed as limiting this application.

[0026] The following description, with reference to the accompanying drawings, describes a vehicle positioning anomaly detection method, apparatus, device, medium, and program according to embodiments of this application.

[0027] Specifically, Figure 1 This is a flowchart illustrating a vehicle positioning anomaly detection method provided in an embodiment of this application.

[0028] like Figure 1 As shown, the vehicle positioning anomaly detection method includes the following steps:

[0029] In step S101, image data during the vehicle's driving process is acquired.

[0030] It is understood that the embodiments of this application can use a camera to capture images of the vehicle in real time during its driving process, so as to facilitate subsequent analysis of the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point.

[0031] In step S102, the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point are analyzed based on the image data.

[0032] The motion parameters include the pitch angle change and pitch acceleration around the first target axis, the roll angle change and roll acceleration around the second target axis, and the yaw angle change and yaw acceleration around the third target axis.

[0033] It is understood that the embodiments of this application can analyze the image data to know the changes of the vehicle in the three dimensions of height, left and right and driving direction, so as to quickly identify whether the vehicle parameters are abnormal.

[0034] In this embodiment of the application, the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point are analyzed based on image data, including: extracting key features from the image data; and identifying the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point based on the key features.

[0035] It is understood that, based on the extracted key features, the embodiments of this application can identify the changing angles and accelerations of the vehicle around the x-axis, y-axis and z-axis, thereby obtaining the precise motion parameters of the vehicle in the three-dimensional coordinate system. This can not only significantly improve the accuracy of vehicle positioning, but also promptly detect and correct any problems that may occur during the positioning process.

[0036] In this embodiment of the application, the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point are identified based on key features, including: identifying the first motion change amount of the vehicle in the first target axis direction, the second motion change amount in the second target axis direction, and the third motion change amount in the third target axis direction; and generating vehicle motion parameters by calculating the angle change amount and acceleration in different dimensions based on the first motion change amount to the third motion change amount using a motion trajectory tracking algorithm.

[0037] It is understood that the embodiments of this application can calculate the angle change and acceleration in different dimensions based on the motion changes in the directions of the first target axis, the second target axis and the third target axis, thereby providing more reliable motion parameters for the vehicle and improving the accuracy of fusion positioning.

[0038] It should be noted that the first target axis is the X-axis, the second target axis is the Y-axis, and the third target axis is the Z-axis, without any specific restrictions.

[0039] In step S103, the motion parameters are compared with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal.

[0040] It is understood that the embodiments of this application can effectively identify and correct positioning deviations caused by sensor errors by comparing image analysis results with sensor data. This not only enhances the accuracy and reliability of the vehicle positioning system, but also improves its ability to cope with emergencies, providing users with a smoother and more reliable navigation experience.

[0041] In this embodiment of the application, comparing motion parameters with the corresponding parameters output in real time by the vehicle sensor to determine whether the vehicle positioning is abnormal includes: if the deviation between the motion parameters and the corresponding parameters output in real time by the vehicle sensor is lower than a preset threshold, the vehicle positioning is determined to be normal; otherwise, the vehicle positioning is determined to be abnormal.

[0042] The preset threshold can be set according to actual needs without specific limitations.

[0043] It is understood that the embodiments of this application can ensure high accuracy and high reliability of vehicle positioning by carefully comparing motion parameters and sensor data, while also improving the system's reaction speed and ability to deal with emergencies, thereby providing users with a safer and smoother driving experience.

[0044] In step S104, if the vehicle positioning is abnormal, the vehicle is controlled to switch from the current positioning mode to the target positioning mode.

[0045] The target positioning mode can be GPS positioning, without any specific restrictions.

[0046] It is understood that the embodiments of this application can immediately take measures to switch the positioning mode once an abnormality in vehicle positioning is confirmed, which can effectively correct the position deviation and prevent further navigation errors. It can not only react quickly when an abnormality in positioning is detected, ensuring the continuity and accuracy of navigation, but also improve the overall system reliability and user satisfaction.

[0047] In this embodiment of the application, it also includes: continuously monitoring the deviation value between the vehicle body sensor data and the image analysis results, and automatically switching back to the current positioning mode when the deviation value recovers to within a preset threshold.

[0048] It is understood that the embodiments of this application can maximize the use of the advantages of various types of data by continuously monitoring and adjusting the positioning mode in a timely manner, thereby improving the accuracy of navigation while ensuring safety, and achieving the goal of improving system stability and adaptability while ensuring navigation accuracy.

[0049] The vehicle positioning anomaly detection method proposed in this application captures images of the vehicle in real time during its driving process. Based on the processed images, the system analyzes the vehicle's motion parameters in a three-dimensional coordinate system, including the changing angles and accelerations around the x, y, and z axes. The vehicle motion parameters obtained from the images are compared with the data output by the vehicle sensors. If there is a significant difference between the two, it indicates that the vehicle may have a positioning anomaly. Once a positioning anomaly is confirmed, the system automatically stops using the current fusion positioning method to avoid navigation deviations caused by incorrect vehicle parameters. This effectively improves the detection capability and efficiency of positioning anomalies during real-time vehicle navigation, ensuring the real-time, accurate, and reliable nature of positioning information. Timely measures are taken when positioning anomalies are detected, reducing potential risks caused by delayed corrections and improving the user experience.

[0050] The following will combine Figure 2-4 The vehicle positioning anomaly detection method of this application is described in detail, and the specific steps are as follows:

[0051] Step 1: Capture driving footage in real time using a camera, analyze and record image changes at specific points in time, and analyze the vehicle's movement. Figure 2 The changes are shown in the x, y, and z directions.

[0052] (1) Determine the angle and acceleration of the vehicle around the x-axis, and name the values ​​x1 and x2 for the time being. The positive direction of the x-axis points to the right side of the vehicle. Since the vehicle is making a pitching motion when rotating around the x-axis, it is also called the pitch axis.

[0053] (2) Determine the angle and acceleration of the vehicle around the y-axis. The obtained values ​​are temporarily named y1 and y2. The positive direction of the y-axis points forward of the vehicle. Because the vehicle is rolling when rotating around the y-axis, it is also called the roll axis.

[0054] (3) Determine the angle and acceleration of the vehicle around the z-axis. The obtained values ​​are temporarily named z1 and z2. The z-axis square points to the top of the vehicle. Because the vehicle is yawing when rolling around the Z-axis, it is also called the yaw axis.

[0055] (4) It should be noted that image recognition is mainly based on images captured by a camera. First, the image undergoes denoising, transformation, and smoothing operations to make its features more prominent. Second, feature extraction and selection are performed to identify important features and eliminate irrelevant information. Finally, the changes in image features across various dimensions are compared to obtain the results, such as... Figure 3 .

[0056] Step 2: Based on the image recognition analysis obtained in Step 1, compare the x, y, and z axis data transmitted by the accelerometer sensor and gyroscope sensor on the vehicle body at the same time. The comparison criteria are as follows:

[0057] (1) Compare the data obtained in the first step, x1, y1, z1, with the x, y, z axis data of the gyroscope to see if they are equal;

[0058] (2) Compare the data obtained in the first step, x2, y2, z2, with the x, y, z axis data of the accelerometer to see if they are equal;

[0059] Step 3: Compare the vehicle speed data obtained from the image recognition analysis in Step 1 with the vehicle's transmitted speed.

[0060] Step 4: After comparing the data in steps 2 and 3 above, it can be confirmed whether the data transmitted by the vehicle is correct. If any abnormalities are found, immediately stop these data from participating in the fusion positioning calculation and switch to pure GPS positioning mode. Figure 4 .

[0061] In summary, this application uses a camera to capture driving footage and analyzes image changes to determine the vehicle's changes in three dimensions: height, left and right, and direction of travel. This includes, but is not limited to, identifying vehicle bumps, forward or reverse movement, and turning. The accuracy of the data transmitted by the vehicle body is determined by comparing these parameters. If anomalies are detected in the vehicle body parameters, the current positioning method is discontinued, and a positioning method using only GPS location information is switched to. By combining road scene capture and image analysis using the vehicle's camera, current road changes can be effectively identified and compared with the vehicle body parameters at that time, thereby effectively assessing the accuracy of the vehicle body parameters. In fusion positioning, this scheme provides an effective method for correcting positioning anomalies, improving the yaw problem in real-time vehicle navigation.

[0062] Next, the vehicle positioning anomaly detection device according to the embodiments of this application is described with reference to the accompanying drawings.

[0063] Figure 5 This is a block diagram of a vehicle positioning anomaly detection device according to an embodiment of this application.

[0064] like Figure 5 As shown, the vehicle positioning anomaly detection device 10 includes: an acquisition module 100, an analysis module 200, a comparison module 300, and a control module 400.

[0065] The acquisition module 100 is used to acquire image data during vehicle operation; the parsing module 200 is used to parse vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the image data; the comparison module 300 is used to compare the motion parameters with the corresponding parameters output by the vehicle's sensors in real time to determine whether the vehicle positioning is abnormal; and the control module 400 is used to control the vehicle to switch from the current positioning mode to the target positioning mode if the vehicle positioning is abnormal.

[0066] The vehicle positioning anomaly detection device proposed in this application captures images of the vehicle in real time during its driving process. Based on the processed images, it analyzes the vehicle's motion parameters in a three-dimensional coordinate system, including the changing angles and accelerations around the x, y, and z axes. The vehicle motion parameters obtained from the images are compared with the data output by the vehicle sensors. If there is a significant difference between the two, it indicates that the vehicle may have a positioning anomaly. Once a positioning anomaly is confirmed, the system automatically stops using the current fusion positioning method to avoid navigation deviations caused by incorrect vehicle parameters. This effectively improves the detection capability and efficiency of positioning anomalies during real-time vehicle navigation, ensuring the real-time, accurate, and reliable nature of positioning information. It takes timely measures when positioning anomalies are detected, reducing the potential risks caused by delayed corrections and improving the user experience.

[0067] Figure 6A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0068] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0069] When the processor 602 executes the program, it implements the vehicle positioning anomaly detection method provided in the above embodiments.

[0070] Furthermore, electronic devices also include:

[0071] Communication interface 603 is used for communication between memory 601 and processor 602.

[0072] The memory 601 is used to store computer programs that can run on the processor 602.

[0073] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0074] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0075] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0076] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0077] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the vehicle positioning anomaly detection method described above.

[0078] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the vehicle positioning anomaly detection method described above.

[0079] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0080] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0081] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0082] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0083] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A vehicle positioning abnormality detection method characterized by comprising: Includes the following steps: Acquire image data during vehicle movement; Based on the image data, analyze the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point; The motion parameters are compared with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal. If the vehicle location is abnormal, control the vehicle to switch from the current location mode to the target location mode; The step of analyzing vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the image data includes: extracting key features from the image data; and identifying vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle's center point based on the key features. The step of identifying vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point based on the key features includes: identifying the first motion change amount, the second motion change amount, and the third motion change amount in the third target axis direction of the vehicle in the key features; and generating vehicle motion parameters by calculating the angle change amount and acceleration in different dimensions based on the first motion change amount to the third motion change amount using a motion trajectory tracking algorithm. The motion parameters include the pitch angle change and pitch acceleration about the first target axis, the roll angle change and roll acceleration about the second target axis, and the yaw angle change and yaw acceleration about the third target axis.

2. The vehicle positioning abnormality detection method according to claim 1, characterized by, The step of comparing the motion parameters with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal includes: If the deviation between the motion parameters and the corresponding parameters output by the vehicle sensors in real time is lower than a preset threshold, the vehicle positioning is determined to be normal; otherwise, the vehicle positioning is determined to be abnormal.

3. The vehicle positioning abnormality detection method according to claim 2, characterized by, Also includes: The system continuously monitors the deviation between the corresponding parameters output by the vehicle's sensor data and the motion parameters. When the deviation returns to within a preset threshold, it automatically switches back to the current positioning mode.

4. A vehicle positioning anomaly detection device, characterized in that, The device is implemented based on the vehicle positioning anomaly detection method according to any one of claims 1-3, and includes the following modules: The acquisition module is used to acquire image data during the vehicle's movement. The analysis module is used to analyze the vehicle motion parameters in a three-dimensional coordinate system centered on the vehicle body center point based on the image data; The comparison module is used to compare the motion parameters with the corresponding parameters output in real time by the vehicle sensors to determine whether the vehicle positioning is abnormal. The control module is used to control the vehicle to switch from the current positioning mode to the target positioning mode if the vehicle positioning is abnormal.

5. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle positioning anomaly detection method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the vehicle positioning anomaly detection method as described in any one of claims 1-3.

7. A computer program product, characterized in that, The system includes a computer program, which, when executed by a processor, is used to implement the vehicle positioning anomaly detection method according to any one of claims 1-3.

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