Fault Detection Method, Device and System

Abstract

An embodiment of the present application provides a fault detection method, device and system, which relate to the field of autonomous driving technology and can solve the technical problems of high production cost and high system design complexity in the existing navigation system, where the measurement values of each measurement source are detected for faults by using a multi-backup scheme for measurement sources. The method includes: obtaining N measurement values corresponding to M measurement sources; performing parallel filtering processing on the N measurement values by using a first filter bank, and determining whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; where the first filter bank includes one main filter and N first filters; if there are faulty measurement values, then determining the faulty measurement values among the N measurement values by using a second filter bank; where the second filter bank includes (N-1) second filters corresponding to each first filter.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving, and in particular, to a fault detection method, device and system. Background Art

[0002] Autonomous driving refers to the cooperation of artificial intelligence, vision computing, radar, monitoring devices, inertial navigation system (INS), global navigation satellite system (GNSS), etc., enabling motor vehicles to automatically and safely drive on the road. In autonomous driving, high-precision maps and high-precision positioning are the decision-making basis for autonomous driving, and a combined positioning system mainly based on the INS system and the GNSS system is a necessary means to achieve high-precision positioning in all scenarios.

[0003] Among them, positioning a motor vehicle mainly based on the INS system and the GNSS system may include: positioning the motor vehicle according to the measurement values of the measurement sources of the INS system to obtain a positioning result. The INS system is not affected by external interference, has a high positioning frequency, high positioning accuracy in a short time, and can output comprehensive positioning information. However, since the output error of the INS system will accumulate over time, the positioning result of the INS system can be assisted and corrected by the measurement values of the measurement sources of the GNSS system or other measurement sources that can perform global positioning on the motor vehicle to improve the positioning accuracy and reliability.

[0004] When using the measurement values of the INS system to position a motor vehicle, if the measurement values output by the measurement sources of the INS system are faulty, it will affect the positioning accuracy and reliability. To avoid the influence of faulty measurement values of the INS system on the positioning accuracy and reliability, the existing INS system can adopt a multi-backup scheme for measurement sources, and detect faults in the measurement values by setting multiple identical measurement sources. When two identical measurement sources are set, it can be detected whether the measurement values corresponding to the measurement source are faulty. When three identical measurement sources are set, it can be detected which measurement source's measurement values are faulty, thereby realizing fault detection and improving the positioning accuracy. However, setting multiple identical measurement sources will increase the production cost and the complexity of system design. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a fault detection method, device and system, which can improve the technical problems in the existing navigation system that the production cost is relatively high and the system design complexity is relatively high due to the adoption of the multi-backup scheme for measurement sources to detect faults in the measurement values of each measurement source.

[0006] In a first aspect, an embodiment of the present application provides a fault detection method, which includes: obtaining N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include inertial navigation system (INS) measurement values; performing parallel filtering processing on the N measurement values by using a first filter bank, and determining whether there are faulty measurement values among the N measurement values according to the processing results of the respective filters included in the first filter bank; if there are faulty measurement values, then determining the faulty measurement values among the N measurement values by using a second filter bank; where the first filter bank includes one main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include (N - 1) measurement values; the input parameters of different first filters are at least partially different; the second filter bank includes (N - 1) second filters corresponding to each first filter, and the input parameters of each second filter include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own first filter; the input parameters of different second filters are at least partially different.

[0007] Based on the first aspect, it is possible to determine whether there are faulty measurement values among the N measurement values of M measurement sources through the first filter bank, and it is possible to determine which specific measurement source has a fault through the second filter bank. Compared with the scheme of using multiple backups of measurement sources, the production cost can be reduced and the system design complexity can be reduced.

[0008] In a possible design, the processing result of the first filter whose input parameters do not include faulty measurement values is used as the output result.

[0009] Based on this possible design, by using the processing result of the first filter whose input parameters do not include faulty measurement values as the output result, the positioning accuracy and reliability can be improved.

[0010] In a possible design, obtain the state estimate and covariance matrix corresponding to the first filter whose input parameters do not include faulty measurement values; according to the state estimate and covariance matrix, adjust the state estimates and covariance matrices of the main filter, each first filter other than the first filter whose input parameters do not include faulty measurement values, and the second filter whose input parameters include faulty measurement values.

[0011] Based on this possible design, by adjusting the main filter, the first filter, and the second filter that include faulty measurement values according to the state estimate and covariance matrix corresponding to the first filter whose input parameters do not include faulty measurement values, the respective filters that include faulty measurement values can be corrected, and the reliability of the processing results of the respective filters can be improved.

[0012] In a possible design, before using the first filter bank to perform parallel filtering processing on N measurement values, the method further includes: preprocessing the N measurement values; where the preprocessing includes converting the N measurement values into the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values.

[0013] Based on this possible design, by preprocessing each measurement value and using each filter to process the preprocessed measurement values, the accuracy and reliability of fault detection can be improved.

[0014] In a possible design, the main filter processes N measurement values to obtain the processing result of the main filter; N first filters respectively process the measurement values to obtain the processing result of each first filter; the processing result of the main filter is compared with the processing result of each first filter to obtain the first difference corresponding to each first filter; it is determined whether there is a first difference greater than or equal to the first threshold; if so, it is determined that there are faulty measurement values.

[0015] Based on this possible design, by comparing the processing result of the main filter with the processing results of each first filter, it can be determined whether there are faulty measurement values among the N measurement values, providing a feasible solution for fault detection.

[0016] In a possible design, each second filter processes the measurement values to obtain the processing result of each second filter; the processing result of each first filter is compared with the processing results of the (N - 1) second filters corresponding to the first filter to obtain the second difference corresponding to each second filter; it is determined whether there is a first filter for which the second differences corresponding to each second filter are all less than the second threshold; if there is, the measurement values not included in the input parameters of the first filter are determined as faulty measurement values.

[0017] Based on this possible design, by comparing the processing result of the first filter with the processing results of each second filter, it can be determined which of the N measurement values are faulty, providing a feasible solution for fault isolation.

[0018] In a possible design, the main filter processes N measurement values to obtain the state estimate and covariance matrix corresponding to the main filter; each first filter and each second filter are initialized according to the state estimate and covariance matrix.

[0019] Based on this possible design, by initializing each first filter and each second filter according to the state estimate and covariance matrix corresponding to the main filter, the problem that some first filters and second filters cannot be initialized independently can be solved.

[0020] In a possible design, when there are no faulty measurement values, the processing result of the main filter is used as the output result.

[0021] Based on the possible design, when there are no faulty measurement values, compared with the first filter and the second filter, the main filter includes more measurement values. Using the processing result of the main filter as the output result can improve the positioning accuracy and reliability.

[0022] In a possible design, the state estimate and covariance matrix of the main filter are obtained periodically; according to the state estimate and covariance matrix corresponding to the main filter, the state estimates and covariance matrices of each first filter and each second filter are adjusted.

[0023] Based on this possible design, by adjusting and correcting each first filter and each second filter according to the state estimate and covariance matrix of the main filter, the reliability of the processing results of each filter can be improved.

[0024] In a possible design, when the measurement values are updated, the main filter, the first filter, and the second filter with input parameters including the measurement values are updated.

[0025] Based on this possible design, by updating each filter in a timely manner when the measurement values are updated, the accuracy and reliability of the processing results of each filter can be improved.

[0026] In a possible design, the measurement sources include at least four or more of the following: Global Navigation Satellite System (GNSS) measurement source, Real-Time Kinematic (RTK), Inertial Measurement Unit (IMU), Wheel Speed Sensor (WSS), Long Baseline Locator (LBL), Vector Semantic Localization (VSL).

[0027] Based on this possible design, the fault detection of the measurement values can be achieved based on the measurement values of at least four measurement sources, reducing the system design complexity.

[0028] In a second aspect, a fault detection device is provided. The fault detection device can implement the functions performed in the above first aspect or any possible design of the first aspect. The functions can be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. Such as a receiving module, a processing module, and a sending module. The receiving module is used to obtain N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include inertial navigation system (INS) measurement values. The processing module is used to perform parallel filtering processing on the N measurement values using a first filter bank, and determine whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; if there are faulty measurement values, use a second filter bank to determine the faulty measurement values among the N measurement values; where the first filter bank includes a main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include (N - 1) measurement values; the input parameters of different first filters are at least one different; the second filter bank includes (N - 1) second filters corresponding to each first filter, and the input parameters of each second filter include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own first filter; the input parameters of different second filters are at least one different.

[0029] Among them, the specific implementation manner of the fault detection device can refer to the behavioral functions in the fault detection method provided in the first aspect or any possible design of the first aspect. Based on the fault detection device described in the second aspect, through the first filter bank, it can be determined whether there are faulty measurement values among the N measurement values of M measurement sources, and through the second filter bank, it can be determined which specific measurement source has a fault. Compared with the scheme of using multiple backups of measurement sources, the production cost can be reduced and the system design complexity can be reduced.

[0030] In a possible design, the device further includes a sending module; the sending module is further used to use the processing result of the first filter whose input parameters do not include faulty measurement values as the output result.

[0031] Based on this possible design, by using the processing result of the first filter whose input parameters do not include faulty measurement values as the output result, the positioning accuracy and reliability can be improved.

[0032] In a possible design, the processing module is further used to obtain the state estimate and covariance matrix corresponding to the first filter whose input parameters do not include faulty measurement values; the processing module is further used to adjust the state estimate and covariance matrix of the main filter, each first filter except the first filter whose input parameters do not include faulty measurement values, and the second filter whose input parameters include faulty measurement values according to the state estimate and covariance matrix.

[0033] Based on this possible design, by adjusting the main filter, the first filter, and the second filter that include fault measurement values according to the state estimation and covariance matrix corresponding to the first filter that does not include fault measurement values for the input parameters, each filter that includes fault measurement values can be corrected, and the reliability of the processing results of each filter can be improved.

[0034] In a possible design, the processing module is further configured to preprocess N measurement values; wherein, the preprocessing includes converting the N measurement values into the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values.

[0035] Based on this possible design, by preprocessing each measurement value and using each filter to process the preprocessed measurement values, the accuracy and reliability of fault detection can be improved.

[0036] In a possible design, the processing module is specifically configured to process N measurement values through the main filter to obtain the processing result of the main filter; process the measurement values through N first filters respectively to obtain the processing result of each first filter; compare the processing result of the main filter with the processing results of each first filter to obtain the first difference corresponding to each first filter; determine whether there is a first difference greater than or equal to the first threshold; if so, determine that there are fault measurement values.

[0037] Based on this possible design, by comparing the processing result of the main filter with the processing results of each first filter, it can be determined whether there are fault measurement values among the N measurement values, providing a feasible solution for fault detection.

[0038] In a possible design, the processing module is specifically further configured to: process the measurement values through each second filter to obtain the processing result of each second filter; compare the processing result of each first filter with the processing results of the (N - 1) second filters corresponding to the first filter to obtain the second difference corresponding to each second filter; determine whether there is a first filter for which the second differences corresponding to each second filter are all less than the second threshold; if there is, determine the measurement value not included in the input parameters of the first filter as a fault measurement value.

[0039] Based on this possible design, by comparing the processing result of the first filter with the processing results of each second filter, it can be determined the measurement values with faults among the N measurement values, providing a feasible solution for fault isolation.

[0040] In a possible design, the processing module is further configured to process N measurement values through a main filter to obtain the state estimation and covariance matrix corresponding to the main filter; and initialize each first filter and each second filter according to the state estimation and covariance matrix.

[0041] Based on this possible design, by initializing each first filter and each second filter according to the state estimation and covariance matrix corresponding to the main filter, the problem that some first filters and second filters cannot be initialized independently can be solved.

[0042] In a possible design, the device further includes a sending module; the sending module is further configured to use the processing result of the main filter as the output result when there are no faulty measurement values.

[0043] Based on the possible design, when there are no faulty measurement values, compared with the first filter and the second filter, the main filter includes more measurement values. Using the processing result of the main filter as the output result can improve the positioning accuracy and reliability.

[0044] In a possible design, the processing module is further configured to periodically obtain the state estimation and covariance matrix of the main filter; and adjust the state estimation and covariance matrix of each first filter and each second filter according to the state estimation and covariance matrix corresponding to the main filter.

[0045] Based on this possible design, by adjusting and correcting each first filter and each second filter according to the state estimation and covariance matrix of the main filter, the reliability of the processing results of each filter can be improved.

[0046] In a possible design, the processing module is further configured to update the main filter, the first filter, and the second filter whose input parameters include measurement values when the measurement values are updated.

[0047] Based on this possible design, by updating each filter in a timely manner when the measurement values are updated, the accuracy and reliability of the processing results of each filter can be improved.

[0048] In a possible design, the measurement sources include at least four or more of the following: Global Navigation Satellite System (GNSS) measurement source, Real-Time Kinematic (RTK), Inertial Measurement Unit (IMU), Wheel Speed Sensor (WSS), Laser-Based Localization (LBL), and Vector Semantic Localization (VSL).

[0049] Based on this possible design, the fault detection of the measurement values can be achieved based on the measurement values of at least four measurement sources, reducing the system design complexity.

[0050] In a third aspect, a fault detection device is provided, which may include: a main filter, N first filters, (N - 1) second filters corresponding to each first filter, and a processor; wherein the processor is communicatively connected to the main filter, each first filter, and each second filter respectively; N is an integer greater than or equal to 4; the main filter is configured to perform parallel filtering processing on N measurement values corresponding to M measurement sources to obtain a processing result of the main filter; wherein the input parameters of the main filter include the N measurement values; the N measurement values include Inertial Navigation System (INS) measurement values; the N first filters are configured to perform parallel filtering processing on (N - 1) measurement values respectively to obtain a processing result of each first filter; wherein the input parameters of each first filter include (N - 1) measurement values; the input parameters of different first filters are at least partially different; the (N - 1) second filters corresponding to each first filter are configured to perform parallel filtering processing on (N - 2) measurement values respectively to obtain a processing result of each second filter; wherein the input parameters of each second filter include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own corresponding first filter; the input parameters of different second filters are at least partially different; the processor is configured to determine whether there are faulty measurement values among the N measurement values according to the processing result of the main filter and the processing results of the N first filters; if there are faulty measurement values, then determine the faulty measurement values among the N measurement values by using the processing results of the (N - 1) second filters corresponding to each first filter.

[0051] Wherein, for the specific implementation manner of this fault detection device, reference may be made to the behavioral functions in the fault detection method provided in the first aspect or any possible design of the first aspect. Based on the fault detection device described in the third aspect, the processor can determine whether there are faulty measurement values among the N measurement values of the M measurement sources according to the processing results of the main filter and the N first filters, and can determine which specific measurement source has a fault according to the processing results of the (N - 1) second filters corresponding to each first filter. Compared with the scheme of using multiple backups of measurement sources, it can reduce production costs and reduce the complexity of system design.

[0052] Fourth aspect, a fault detection device is provided. The fault detection device can be a chip or a system-on-chip. The device can implement the functions performed by the above aspects or each possible design, and the functions can be implemented by hardware. In a possible design, the fault detection device may include: a transceiver and a processor. The transceiver and the processor can be used to support the fault detection device to implement the functions involved in the first aspect or any possible design of the first aspect. For example: the transceiver can be used to obtain N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include inertial navigation system (INS) measurement values; the processor can be used to perform parallel filtering processing on the N measurement values by using a first filter bank, and determine whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; where the first filter bank includes one main filter and N first filters; the input parameters of the main filter include N measurement values; the input parameters of each first filter include (N-1) measurement values; at least one of the input parameters of different first filters is different; the processor can also be used to, if there are faulty measurement values, use a second filter bank to determine the faulty measurement values among the N measurement values; where the second filter bank includes (N-1) second filters corresponding to each first filter, and the input parameters of each second filter include (N-2) measurement values among the (N-1) measurement values corresponding to its own first filter; at least one of the input parameters of different second filters is different. In yet another possible design, the fault detection device may further include a memory, and the memory is used to store necessary computer execution instructions and data of the fault detection device. When the fault detection device runs, the transceiver and the processor execute the computer execution instructions stored in the memory, so that the fault detection device executes the fault detection method as described in the first aspect or any possible design of the first aspect.

[0053] Among them, the specific implementation manner of the fault detection device can refer to the behavioral functions of the fault detection method provided in the first aspect or any possible design of the first aspect.

[0054] Fifth aspect, a fault detection device is provided. The fault detection device includes one or more processors and one or more memories; one or more memories are coupled to one or more processors, and one or more memories are used to store computer program code or computer instructions; when one or more processors execute the computer instructions, the fault detection device is caused to execute the fault detection method as described in the first aspect or any possible design of the first aspect.

[0055] In a sixth aspect, a computer-readable storage medium is provided, which stores computer instructions or programs. When the computer instructions or programs are run on a computer, the computer is caused to execute the fault detection method as described in the first aspect or any possible design of the first aspect.

[0056] In a seventh aspect, a computer program product containing instructions is provided. When it runs on a computer, the computer is caused to execute the fault detection method as described in the first aspect or any possible design of the first aspect.

[0057] In an eighth aspect, a chip system is provided. The chip system includes one or more processors and one or more memories; the one or more memories are coupled to the one or more processors, and computer program code or computer instructions are stored in the one or more memories; when the one or more processors execute the computer program code or computer instructions, the chip system is caused to execute the fault detection method as described in the first aspect or any possible design of the first aspect.

[0058] Among them, the technical effects brought by any design manner in the fourth aspect to the eighth aspect can be referred to the technical effects brought by any possible design in the first aspect to the third aspect above, and will not be elaborated.

[0059] In a ninth aspect, a communication system is provided, which includes the fault detection device as described in any one of the second aspect to the fourth aspect.

[0060] In a tenth aspect, an autonomous vehicle is provided, which includes the fault detection device as described in any one of the second aspect to the fourth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of a fault detection system provided by an embodiment of the present application;

[0062] Figure 2 It is a composition structure diagram of a communication device provided by an embodiment of the present application;

[0063] Figure 3 It is a flowchart of a fault detection method provided by an embodiment of the present application;

[0064] Figure 4a It is a schematic structural diagram of a first filter bank provided by an embodiment of the present application;

[0065] Figure 4b It is a schematic structural diagram of a second filter bank provided by an embodiment of the present application;

[0066] Figure 5Flow chart of a fault detection method provided by an embodiment of this application;

[0067] Figure 6 Schematic diagram of the composition of a fault detection device provided by an embodiment of this application. Specific implementation manners

[0068] Before describing the embodiments of this application, the technical terms related to the embodiments of this application are described.

[0069] Navigation system: It may include an inertial navigation system (INS) and a global navigation satellite system (GNSS). Among them, the measurement source of the INS system can measure an object to obtain the positioning result of the object. The GNSS system can achieve three-dimensional positioning of the object based on at least 4 satellites, and assist in correcting the positioning result output by the INS system according to the three-dimensional positioning result, improving the positioning accuracy and reliability.

[0070] Specifically, the INS system may include an inertial measurement unit (IMU), real-time kinematic (RTK), a wheel speed sensor (WSS), etc. as measurement sources that can measure an object and obtain the positioning result of the object through calculation.

[0071] Among them, the IMU can obtain information such as the acceleration and angular velocity of the object, and obtain attitude information such as the position and velocity of the object through integral calculation, constituting the INS system. RTK can obtain the positioning result of the object based on the GNSS measurement value of the object by using the carrier phase differential technology, improving the positioning accuracy. The WSS can obtain the velocity information of the object and use this information to correct the positioning result of the object, improving the positioning accuracy.

[0072] Specifically, the GNSS system may include satellite navigation systems such as a global positioning system (GPS), a Beidou navigation satellite system (BDS), a Global Navigation Satellite System (GLONASS), and a Galileo satellite navigation system (Galileo) to achieve global positioning of the object.

[0073] In addition to the above GNSS system, other global positioning measurement sources can be used to achieve global positioning of an object. For example: lidar based localization (LBL) that matches with a laser map, vector semantic localization (VSL) that matches with a high-precision map or a vector map, etc., which are measurement sources that can perform global positioning on an object.

[0074] When using the INS system and the GNSS system to position an object, if there is a fault in the measurement value output by the measurement source of the INS system, it will affect the correction of the positioning result, and thus affect the positioning accuracy and reliability. To avoid the influence of the fault in the measurement value of the INS system on the correction of the positioning result, the INS system can adopt a multi-backup scheme for the measurement source, and detect the fault of the measurement value by setting multiple identical measurement sources. When two identical measurement sources are set, it can be detected whether the measurement value corresponding to the measurement source is faulty. When three identical measurement sources are set, it can be detected which specific measurement source has a faulty measurement value, so as to achieve fault detection and improve the positioning accuracy. However, setting multiple identical measurement sources will increase the production cost and the complexity of system design.

[0075] In addition, when using the INS system and the GNSS system to position an object, the measurement value output by the measurement source of the GNSS system may also have a fault, which affects the correction of the positioning result, thereby affecting the positioning accuracy and reliability. Therefore, it is also necessary to detect the fault of the measurement value of the measurement source of the GNSS system.

[0076] To solve the above problems, an embodiment of the present application provides a fault detection method, which includes: obtaining N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include INS measurement values; using a first filter bank to perform parallel filtering processing on the N measurement values, and determining whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; if there are faulty measurement values, using a second filter bank to determine the faulty measurement values among the N measurement values; where the first filter bank includes one main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include (N - 1) measurement values; the input parameters of different first filters are at least one different; the second filter bank includes (N - 1) second filters corresponding to each first filter, and the input parameters of each second filter include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own first filter; the input parameters of different second filters are at least one different. In the embodiment of the present application, through the first filter bank, it can be determined whether there are faulty measurement values among the N measurement values of M measurement sources in the navigation system, and through the second filter bank, it can be determined which specific measurement source has a fault. Compared with the scheme of using multiple backups of measurement sources, the production cost can be reduced and the system design complexity can be reduced.

[0077] The implementation manners of the embodiments of the present application will be described in detail below with reference to the accompanying drawings of the specification.

[0078] The fault detection method provided by the embodiment of the present application can be used in any fault detection system for positioning an object. The fault detection system can include multiple measurement sources of the above navigation system and a fault detection device.

[0079] Figure 1 FIG. is a schematic diagram of a fault detection system provided by an embodiment of the present application, as Figure 1 shown, the fault detection system 100 can include multiple measurement sources of the navigation system 101 and a fault detection device 102.

[0080] Among them, the navigation system 101 can include at least four measurement sources, and the at least four measurement sources can include at least one measurement source of the INS system.

[0081] Specifically, in the navigation system 101, the measurement source of the INS system is used to measure an object to obtain the positioning result of the object, and the measurement source of the GNSS system is used to obtain the GNSS measurement value of the object and correct the positioning result of the object based on the GNSS measurement value to improve the positioning accuracy. The fault detection device can be used to perform fault detection on the measurement values of each measurement source of the navigation system. By isolating the faulty measurement values, the positioning accuracy and reliability can be improved.

[0082] In specific implementation, Figure 1 as shown, for example, each measurement source of the navigation system and the fault detection device can all adopt Figure 2 the shown component structure, or include Figure 2 the shown components. Figure 2 The figure is a schematic diagram of the composition of a communication device 200 provided by an embodiment of the present application. The communication device 200 can be a measurement source or a chip or system-on-chip in the measurement source; it can also be a fault detection device or a chip or system-on-chip in the fault detection device. As Figure 2 shown, the communication device 200 includes a processor 201, a transceiver 202, and a communication line 203.

[0083] Furthermore, the communication device 200 may further include a memory 204. Among them, the processor 201, the memory 204, and the transceiver 202 can be connected through the communication line 203.

[0084] Among them, the processor 201 is a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 can also be other devices with processing functions, such as circuits, devices, or software modules, without limitation.

[0085] The transceiver 202 is used to communicate with other devices or other communication networks. The other communication network can be an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc. The transceiver 202 can be a module, a circuit, a transceiver, or any device capable of implementing communication.

[0086] The communication line 203 is used to transmit information between the components included in the communication device 200.

[0087] The memory 204 is used to store instructions. Among them, the instructions can be computer programs.

[0088] Among them, the memory 204 can be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, without limitation.

[0089] It should be noted that the memory 204 can exist independently of the processor 201 or be integrated with the processor 201. The memory 204 can be used to store instructions, program codes, or some data, etc. The memory 204 can be located inside the communication device 200 or outside the communication device 200, without limitation. The processor 201 is configured to execute the instructions stored in the memory 204 to implement the fault detection method provided in the following embodiments of the present application.

[0090] In one example, the processor 201 can include one or more CPUs, such as Figure 2 CPU0 and CPU1 in

[0091] As an alternative implementation, the communication device 200 includes multiple processors. For example, in addition to Figure 2 the processor 201 in

[0092] As an alternative implementation, the communication device 200 further includes an output device 205 and an input device 206. Exemplarily, the input device 206 is a device such as a keyboard, a mouse, a microphone, or a joystick, and the output device 205 is a device such as a display screen or a speaker.

[0093] It should be noted that the communication device 200 can be a desktop computer, a laptop computer, a network server, a mobile phone, a tablet computer, a wireless terminal, an embedded device, a chip system, or a device with a Figure 2 similar structure in Figure 3 In addition, the component structure shown in Figure 2 does not constitute a limitation on the communication device. Except for the

[0094] In the embodiments of the present application, the chip system may be composed of chips, or may include chips and other discrete devices.

[0095] In addition, actions, terms, etc. involved between the embodiments of the present application can be referred to each other without limitation. The message names or parameter names in the messages exchanged between devices in the embodiments of the present application are only examples, and other names can also be used in specific implementations without limitation.

[0096] The following Figure 1 describes the fault detection method provided by the embodiments of the present application in combination with the Figure 2 shown fault detection system. Among them, the measurement source can be any measurement source in the fault detection system, and the fault detection device can be any fault detection device in the fault detection system. The measurement sources and fault detection devices described in the following embodiments can all have

[0097] Figure 3 is a flowchart of a fault detection method provided by the embodiments of the present application. As Figure 3 shown, the method may include:

[0098] Step 301, obtain N measurement values corresponding to M measurement sources.

[0099] Wherein, N is an integer greater than or equal to 4; the N measurement values may include at least one INS measurement value.

[0100] Specifically, M is greater than or equal to N, and the fault detection device can obtain M measurement values corresponding to the object according to M measurement sources of the navigation system. When it is ensured that a certain measurement source will not fail, the measurement value of this measurement source can also be combined with the measurement values of other measurement sources to obtain measurement values with a quantity less than M.

[0101] For example, taking the measurement sources of the navigation system including LBL, VSL, RTK, IMU, and WSS as an example, the fault detection device can obtain the measurement value of LBL, the measurement value of VSL, the measurement value of RTK, the measurement value of IMU, and the measurement value of WSS. Assuming that it can be ensured that VSL will not fail, the measurement value of VSL can be combined with other measurement values, such as combining the measurement value of VSL with the measurement value of WSS, so as to reduce the number of measurement values and reduce the processing burden of the fault detection device.

[0102] It should be noted that the measurement value of LBL can also be briefly described as LBL, the measurement value of VSL can also be briefly described as VSL, the measurement value of RTK can also be briefly described as RTK, the measurement value of IMU can also be briefly described as IMU, and the measurement value of WSS can also be briefly described as WSS.

[0103] Optionally, before the fault detection device performs fault detection on N measurement values, preprocessing is performed on the N measurement values.

[0104] Exemplarily, the preprocessing may include converting the N measurement values into the same coordinate system.

[0105] Specifically, when different measurement sources measure an object, different coordinate systems may be used to record the measurement values corresponding to the object. By converting each measurement value into the same coordinate system, spatial alignment and temporal alignment of the N measurement values can be achieved, which can reduce the computational complexity and relieve the processing burden of the fault detection device.

[0106] For example, the N measurement values can be converted into the same common coordinate system, such as the Earth coordinate system, the global coordinate system, etc., without limitation.

[0107] Among them, the Earth coordinate system may include the Earth rectangular coordinate system and the Earth geodetic coordinate system. In the Earth rectangular coordinate system, the origin O coincides with the Earth's centroid, the Z-axis points to the Earth's North Pole, the X-axis points to the intersection of the Earth's equatorial plane and the Greenwich meridian, and the Y-axis forms a right-handed coordinate system with XOZ in the equatorial plane. The Earth geodetic coordinate system is also called the geographic coordinate system. In this coordinate system, the center of the Earth ellipsoid coincides with the Earth's centroid, the short axis of the ellipsoid coincides with the Earth's axis of rotation, and the position of a spatial point is expressed as (longitude L, latitude B, height H) in this coordinate system. The global coordinate system is also called the world coordinate system. This coordinate system establishes the reference system required to describe other coordinate systems, that is, the world coordinate system can be used to describe the positions of all other coordinate systems or objects.

[0108] In another example, the preprocessing may further include deleting abnormal measurement values.

[0109] Among them, the abnormal measurement values may include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values.

[0110] For example, taking the range of the angular velocity of the IMU as 0 to 200 rad / s as an example, assuming that the measurement value measured by the IMU at a certain moment is 240 rad / s, it can be determined that this measurement value exceeds the measurement range of the IMU and belongs to an abnormal measurement value, and this measurement value needs to be deleted.

[0111] For another example, taking the historical angular velocities of the IMU as 10 rad / s, 20 rad / s, 30 rad / s, 40 rad / s in sequence as an example, assuming that the angular velocity measured by the IMU at the next moment is 180 rad / s, it can be determined that this measurement value deviates from the measurement trajectory and belongs to an abnormal measurement value, and this measurement value needs to be deleted.

[0112] Optionally, the fault detection device determines whether there are abnormal measurement values among the N measurement values based on the chi-square test method of residuals.

[0113] Specifically, the fault detection device can calculate the residuals and the variances of the residuals in the filters corresponding to each measurement value, and construct a chi-square test statistic based on the residuals and the standard deviations of the residuals. Among them, the degrees of freedom of the chi-square test statistic can be the dimension of the measurement value (for example, taking the measurement value as a 3D measurement value including longitude, latitude, and altitude, the degrees of freedom can be 3). When the chi-square test statistic exceeds the threshold Td corresponding to the preset false alarm rate, it can be determined that there are obvious abnormal measurement values among the N measurement values.

[0114] It should be noted that the specific process of constructing the chi-square test statistic based on the residuals and the standard deviations of the residuals can refer to the description of the chi-square test statistic in the prior art, which will not be elaborated here.

[0115] Step 302: Perform parallel filtering processing on the N measurement values using the first filter bank, and determine whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank. If there are faulty measurement values, execute the following steps 303 and 304. Otherwise, execute the following step 305.

[0116] Among them, the first filter bank includes a main filter and N first filters; the input parameters of the main filter include N measurement values; the input parameters of each first filter include (N - 1) measurement values; at least one of the input parameters of different first filters is different.

[0117] For example, referring to Figure 4a , taking the measurement values obtained by the fault detection device including LBL, VSL, RTK, IMU, and WSS as an example, the first filter bank can include a main filter and 5 first filters. Among them, the input parameters of the main filter can include LBL, VSL, RTK, IMU, and WSS; the input parameters of the first filter 1 can include LBL, VSL, RTK, and IMU; the input parameters of the first filter 2 can include LBL, VSL, RTK, and WSS; the input parameters of the first filter 3 can include LBL, VSL, IMU, and WSS; the input parameters of the first filter 4 can include LBL, RTK, IMU, and WSS; the input parameters of the first filter 5 can include VSL, RTK, IMU, and WSS.

[0118] Specifically, the fault detection device can process N measurement values through a main filter to obtain the processing result of the main filter; process the measurement values corresponding to each of the N first filters through the N first filters respectively to obtain the processing result of each first filter; compare the processing result of the main filter with the processing result of each first filter to obtain the first difference corresponding to each first filter; determine whether there is a first difference greater than or equal to the first threshold; if so, determine that there are faulty measurement values.

[0119] It should be noted that for the processing results of each filter on the input parameters, if there is a faulty measurement value, due to the existence of this faulty measurement value, the difference between the processing results of the input parameters including the faulty measurement value will be small, and the difference between the processing result of the input parameters including the faulty measurement value and the processing result of the input parameters not including the faulty measurement value will be large. According to the comparison between the processing results of the main filter and each first filter, it can be determined whether there is a faulty measurement value.

[0120] Among them, the above-mentioned main filter and each first filter can both be Kalman filters, or other filters that can perform filtering processing on input parameters, without limitation.

[0121] Specifically, the Kalman filter can include a state covariance matrix P, a process noise covariance matrix Q, and a measurement noise covariance matrix R. The state covariance matrix P is a matrix composed of the covariances between the states of an object. The diagonal elements of it are the variances of each state, and the remaining elements are the covariances of the corresponding elements. The covariance matrix P is a multi-dimensional square matrix, and its dimension is the same as the number of states; among them, the state of the object can include state information such as the position of the object and the speed of the object, without limitation. The process noise covariance matrix Q is caused by uncertain noise, and each element of this matrix is respectively the covariance between the element errors of the states of the object. The measurement noise covariance matrix R is caused by the error of the measurement source. When in use, the measurement source can give an accuracy index, and the measurement noise covariance matrix R can be directly obtained according to this accuracy index.

[0122] Exemplarily, referring to Figure 4a, the fault detection device can use the main filter to filter the input parameters LBL, VSL, RTK, IMU, and WSS to obtain the processing result 1 of the main filter; use the first filter 1 to filter the input parameters LBL, VSL, RTK, and IMU to obtain the processing result 11 of the first filter 1; use the first filter 2 to filter the input parameters LBL, VSL, RTK, and WSS to obtain the processing result 12 of the first filter 2; use the first filter 3 to filter the input parameters LBL, VSL, IMU, and WSS to obtain the processing result 13 of the first filter 3; use the first filter 4 to filter the input parameters LBL, RTK, IMU, and WSS to obtain the processing result 14 of the first filter 4; use the first filter 5 to filter the input parameters VSL, RTK, IMU, and WSS to obtain the processing result 15 of the first filter 5. Compare the processing result 1 with the processing result 11 to obtain the first difference 11, compare the processing result 1 with the processing result 12 to obtain the first difference 12, compare the processing result 1 with the processing result 13 to obtain the first difference 13, compare the processing result 1 with the processing result 14 to obtain the first difference 14, and compare the processing result 1 with the processing result 15 to obtain the first difference 15. Determine whether there is a first difference among the first difference 11, the first difference 12, the first difference 13, the first difference 14, and the first difference 15 that is greater than or equal to the first threshold. If so, it is determined that there is a faulty measurement value among the respective measurement values obtained by the fault detection device.

[0123] Step 303: Use the second filter bank to determine the faulty measurement value among the N measurement values.

[0124] Among them, the second filter bank can include (N - 1) second filters corresponding to each first filter, and the input parameters of each second filter can include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own first filter; the input parameters of different second filters are at least one different.

[0125] For example, referring to Figure 4a and Figure 4b , taking the first filter 1 in Figure 4a as an example, the first filter 1 can correspond to 4 second filters. Among them, the input parameters of the second filter 11 can include LBL, VSL, and RTK; the input parameters of the second filter 12 can include LBL, VSL, and IMU; the input parameters of the second filter 13 can include LBL, RTK, and IMU; the input parameters of the second filter 14 can include VSL, RTK, and IMU.

[0126] Specifically, the fault detection device can process the measurement values corresponding to each first filter through each second filter to obtain the processing result of each second filter; and compare the processing result of each first filter with the processing results of the (N - 1) second filters corresponding to the first filter to obtain the second difference corresponding to each second filter; determine whether there is a first filter for which the second differences of each corresponding second filter are all less than the second threshold; if there is, determine the measurement value not included in the input parameters of the first filter as the fault measurement value.

[0127] It should be noted that for the processing results of each filter on the input parameters, if there is a fault in a measurement value, due to the existence of this fault measurement value, the difference between the processing result of the first filter whose input parameters include this fault measurement value and the processing result of the corresponding second filter whose input parameters do not include the fault measurement value will be relatively large; while the differences between the processing results of the first filter whose input parameters do not include this fault measurement value and the processing results of its corresponding second filters are all relatively small. Therefore, the measurement value not included in the input parameters of the first filter for which the second differences of each corresponding second filter are all less than the second threshold can be determined as the fault measurement value.

[0128] Exemplarily, referring to Figure 4a and Figure 4b , the fault detection device can use the second filter 11 corresponding to the first filter 1 to process the input parameters LBL, VSL, and RTK to obtain the processing result 111; use the second filter 12 to process the input parameters LBL, VSL, and IMU to obtain the processing result 112; use the second filter 13 to process the input parameters LBL, RTK, and IMU to obtain the processing result 113; use the second filter 14 to process the input parameters VSL, RTK, and IMU to obtain the processing result 114.

[0129] Similar to the first filter 1, the fault detection device can also perform similar processing using the respective second filters corresponding to the first filter 2 to obtain the processing result 121, the processing result 122, the processing result 123, and the processing result 124; perform similar processing using the respective second filters corresponding to the first filter 3 to obtain the processing result 131, the processing result 132, the processing result 133, and the processing result 134; perform similar processing using the respective second filters corresponding to the first filter 4 to obtain the processing result 141, the processing result 142, the processing result 143, and the processing result 144; perform similar processing using the respective second filters corresponding to the first filter 5 to obtain the processing result 151, the processing result 152, the processing result 153, and the processing result 154.

[0130] The fault detection device can determine the second difference corresponding to each second filter according to the processing result of each first filter and the processing results of the corresponding second filters. That is, the fault detection device can determine the second difference 111 according to the processing result 11 of the first filter 1 and the processing result 111 of the second filter 11; determine the second difference 112 according to the processing result 11 of the first filter 1 and the processing result 112 of the first filter 12; similarly, determine the second difference corresponding to each second filter in turn, and determine the measurement value not included in the input parameter of the first filter with the second difference of each corresponding second filter less than the second threshold as the fault measurement value.

[0131] It should be noted that before the fault detection device uses the above first filter bank and second filter bank to detect faults in N measurement values, the fault detection device can also determine the state estimation and covariance matrix corresponding to the main filter when the main filter processes the N measurement values; initialize each first filter and each second filter according to the state estimation and covariance matrix corresponding to the main filter, so as to avoid independent initialization of the first filter and the second filter.

[0132] Among them, the state estimation can be used to indicate the state information of the object, such as the position, speed, attitude, acceleration, angular velocity and other object motion state information of the object, as well as the measurement source error information, such as the zero bias error of the IMU, the installation error between the sensors of the measurement source, etc. The state estimation corresponds to the above state covariance matrix P, and the state covariance matrix P is used to describe the state estimation.

[0133] It should be noted that each filter in the first filter bank and the second filter bank can be a Kalman filter, or a variant of the Kalman filter, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), etc., and can also be other filters that can filter the measurement values, without limitation.

[0134] Step 304: Use the processing result of the first filter whose input parameter does not include the fault measurement value as the output result.

[0135] Specifically, by using the processing result of the first filter whose input parameter does not include the fault measurement value as the output result, the fault detection device can avoid the influence of the fault measurement value on the output result and improve the positioning accuracy and reliability.

[0136] Furthermore, after the fault detection device uses the processing result of the first filter whose input parameter does not include the fault measurement value as the output result, it can set a synchronization flag or synchronization period for the first filter to identify successful fault isolation.

[0137] Exemplarily, when the successful isolation of a fault is identified by setting a synchronization flag, the fault detection device may identify the measured value not included in the input parameters of the first filter as the fault measured value by setting a synchronization identifier for this first filter and not setting a synchronization identifier for other first filters.

[0138] In another example, the fault detection device may also set a synchronization identifier for each first filter. Among them, the synchronization identifier of the first filter whose input parameters do not include the fault measured value may be set to true, and the synchronization identifiers of the remaining first filters may be set to false.

[0139] For example, the synchronization identifier of the first filter whose input parameters do not include the fault measured value may be set to 1, the synchronization identifiers of the remaining first filters may be set to 0, and the measured value not included in the input parameters of the first filter with the synchronization identifier of 1 is the fault measured value.

[0140] In yet another example, the fault detection device may also set a synchronization period for the first filter whose input parameters do not include the fault measured value. During this period, the measured value not included in the input parameters of this first filter is the fault measured value, so as to avoid the fault detection device frequently using the above-mentioned first filter bank and second filter bank to determine the fault measurement source and reduce the processing burden of the fault detection device.

[0141] Furthermore, after the successful isolation of a fault, the fault detection device may also obtain the state estimate and covariance matrix corresponding to the first filter whose input parameters do not include the fault measured value; according to the state estimate and covariance matrix, adjust the state estimates and covariance matrices of the main filter, each first filter except the first filter whose input parameters do not include the fault measured value, and the second filter whose input parameters include the fault measured value, so as to improve the positioning accuracy and reliability of each filter.

[0142] Exemplarily, the fault detection device may periodically adjust the state estimates and covariance matrices of the main filter, each first filter except the first filter whose input parameters do not include the fault measured value, and the second filter whose input parameters include the fault measured value according to the state estimate and covariance matrix of the first filter whose input parameters do not include the fault measured value.

[0143] Step 305: Use the processing result of the main filter as the output result.

[0144] Specifically, when there is no fault measurement source, the fault detection device may use the processing result of the main filter with more input parameters as the output result to improve the positioning accuracy and reliability.

[0145] Further, after using the processing result of the main filter as the output result, the fault detection device can periodically obtain the state estimation and covariance matrix of the main filter; and adjust the state estimation and covariance matrix of each first filter and each second filter according to the state estimation and covariance matrix corresponding to the main filter, so as to improve the processing accuracy and reliability of each filter.

[0146] It should be noted that in the above steps 301 to 305, when the measurement value of the measurement source is updated, the main filter, the first filter, and the second filter with the measurement value included in the input parameters can be updated, so as to determine the positioning result of the object according to the latest measurement value in real time, and improve the positioning accuracy and reliability.

[0147] Based on the above Figure 3 In the method shown, the fault detection device can determine whether there is a faulty measurement value among the N measurement values of the M measurement sources in the navigation system through the first filter bank, and can determine which measurement source is faulty through the second filter bank. Compared with the scheme of using multiple backups of measurement sources, the production cost can be reduced and the system design complexity can be reduced.

[0148] In addition, in the method shown above Figure 3 the fault detection device can realize fault detection by using parallel filtering technology to process at least four measurement values. When using parallel filtering technology to process GNSS measurement values, at least four GNSS measurement values are required for the second filter and at least six measurement values are required for the main filter to achieve three-dimensional positioning of the object. Compared with this, the embodiment of the present application can improve the positioning accuracy and reliability with fewer measurement values.

[0149] Based on the above Figure 3 In the method shown, the fault detection device can also determine whether each measurement source has completed at least one update of the measurement value before executing the above step 302. If so, fault detection is performed according to the above steps 302 to 305, so as to avoid the fault detection device from performing fault detection frequently and reduce the processing burden of the fault detection device.

[0150] It should be noted that before the fault detection device executes the above step 302, it can also determine whether the object is in a non-stationary state according to the obtained measurement values. If so, fault detection is performed according to the above steps 302 to 305, so as to avoid unnecessary fault detection by the fault detection device when the object is in a stationary state, thereby reducing the processing burden of the fault detection device.

[0151] In the above Figure 3 In the method shown, the fault detection device can perform fault detection on the N measurement values of the navigation system according to the first filter bank and the second filter bank. Refer to the followingFigure 5 When the following Figure 5 shown conditions are met, the fault detection device may Figure 3 adopt the method shown to perform fault detection on N measurement values of the navigation system.

[0152] Figure 5 is a flowchart of a fault detection method provided by an embodiment of the present application. As Figure 5 shown, the method may include:

[0153] Step 501, the fault detection device initializes the main filter.

[0154] Among them, the fault detection device can complete the initialization of the main filter by constructing a covariance matrix for the main filter.

[0155] Specifically, the specific process of constructing the covariance matrix for the main filter can refer to the prior art and will not be elaborated here.

[0156] Step 502, the fault detection device updates the main filter.

[0157] Specifically, after the fault detection device completes the initialization of the main filter, it can update the main filter according to the updated measurement values of the measurement source to improve the positioning accuracy of the processing result of the main filter.

[0158] Step 503, the fault detection device controls the output of the main filter.

[0159] Specifically, the fault detection device can use the processing results of the main filter for each measurement value as the output before performing fault detection on each measurement value.

[0160] Step 504, the fault detection device determines whether the number of measurement values is greater than or equal to 4. If so, execute step 505; otherwise, execute step 509.

[0161] Specifically, for the first filter bank and the second filter bank, it is necessary to ensure that the second filter has at least two input parameters to achieve fault detection of the measurement values. When the number of measurement values is greater than or equal to 4, it can be ensured that the second filter has at least two input parameters, so that the fault detection device can perform fault detection according to the first filter bank and the second filter bank, improving the positioning accuracy. When the number of measurement values is less than 4, the number of input parameters of the second filter is less than 2, and the fault detection device needs to stop fault detection.

[0162] Step 505, the fault detection device determines whether the main filter converges. If so, execute step 506; otherwise, execute step 509.

[0163] Specifically, the fault detection device can use the main filter to process each measurement value, obtain a processing result, and determine whether the main filter converges according to the processing result. If it converges, continue with the fault detection; otherwise, stop the fault detection, thereby improving the reliability of the processing result of the main filter.

[0164] Step 506: The fault detection device initializes the first filter and the second filter.

[0165] Specifically, the fault detection device can refer to the above-mentioned step 303 and initialize the first filter and the second filter according to the state estimation and covariance matrix corresponding to the main filter, thereby avoiding independent initialization of the first filter and the second filter.

[0166] Step 507: The fault detection device updates the first filter and the second filter.

[0167] Specifically, after the fault detection device completes the initialization of the first filter and the second filter, it can update the first filter and the second filter according to the updated measurement values of the measurement source, thereby improving the positioning accuracy of the processing results of the first filter and the second filter.

[0168] Step 508: The fault detection device determines whether the object is stationary. If yes, execute step 509; otherwise, execute step 511.

[0169] Among them, when the object is stationary, the positioning result of the object remains unchanged, and the fault detection device can avoid performing fault detection on the measurement values corresponding to the object, so as to reduce the power consumption of the fault detection device and relieve the processing burden of the fault detection device.

[0170] Specifically, the fault detection device can determine whether the object is stationary according to the measurement values corresponding to the object.

[0171] For example, taking the measurement values corresponding to the object including IMU and WSS as an example, the fault detection device can determine that the object is stationary when both the IMU and the WSS meet the stationary judgment conditions.

[0172] Specifically, the fault detection device can adopt a sliding window algorithm. When the difference between the modulus of the acceleration in the sliding window and the local gravitational acceleration is within a certain threshold range, or the variances of the acceleration and the angular velocity are within a certain range, it can be determined that the IMU meets the stationary judgment conditions. The fault detection device can also determine that the WSS meets the stationary judgment conditions when the wheel speed of the object determined according to the WSS is less than a preset threshold.

[0173] Step 509: The fault detection device stops the fault detection.

[0174] Step 510: The fault detection device switches to the output of the main filter.

[0175] Specifically, when the fault detection device stops performing fault detection on the measured values, the fault detection device may use the processing result of the main filter on the measured values as the output.

[0176] Step 511: The fault detection and isolation device performs fault detection.

[0177] Among them, when the fault detection device determines that the number of measured values is greater than or equal to 4, the main filter converges, and the object is not stationary, fault detection can be performed on the measured values, thereby improving the positioning accuracy.

[0178] Specifically, the fault detection device may perform fault detection by using the method shown in the above step 302, which will not be elaborated here.

[0179] Step 512: The fault detection device determines whether there are faulty measured values. If yes, perform step 513; otherwise, perform step 519.

[0180] Specifically, when it is determined that there are faulty measured values, the fault detection device may further perform the following step 513 to determine the faulty measured values according to the second filter bank. When there are no faulty measured values, the fault detection device may perform the following step 519 to determine whether there is continuous faultlessness and whether the current output is a non-main filter output.

[0181] Step 513: The fault detection device performs fault isolation.

[0182] Specifically, the fault detection device may refer to the above step 303 to determine the faulty measured values according to the second filter bank.

[0183] Step 514: The fault detection device determines whether the isolation is successful. If yes, perform step 515; otherwise, perform step 521.

[0184] Specifically, when it is determined that there are faulty measured values according to step 512, but no faulty measured values are found according to step 513, it can be considered that the isolation fails. When it is determined that there are faulty measured values according to step 512 and faulty measured values are found according to step 513, it can be considered that the isolation is successful.

[0185] When the isolation is successful, step 515 can be performed to use the processing result of the first filter whose input parameters do not include the faulty measured values as the output. When the isolation fails, step 521 can be performed to determine whether there is continuous isolation failure and whether the current output is a non-main filter output.

[0186] Step 515: The fault detection device switches to the output of the first filter whose input parameters do not include the faulty measured values and sets a synchronization flag or synchronization period.

[0187] Specifically, the fault detection device may refer to step 304 above, take the processing result of the first filter whose input parameters do not include the fault measurement value as the output, and set the synchronization flag or synchronization period, which will not be elaborated here.

[0188] Step 516: The fault detection device determines whether the synchronization flag is true or the synchronization period has expired. If so, execute step 517; otherwise, execute step 518.

[0189] Specifically, when the synchronization flag is true or the synchronization period has expired, the fault detection device may synchronize the state estimates and covariance matrices of the filters including the fault measurement values in the first filter bank and the second filter bank according to the state estimate and covariance matrix of the first filter whose input parameters do not include the fault measurement value. When the synchronization flag is not true or the synchronization period has not expired, the fault detection device may smooth the processing result and output it.

[0190] Step 517: The fault detection device synchronizes all filters.

[0191] Step 518: The fault detection device performs queue smoothing on the output.

[0192] Specifically, steps 517 and 518 may refer to the specific descriptions of step 516 above, which will not be elaborated here.

[0193] Step 519: The fault detection device determines whether there is no fault continuously and the output is not from the main filter. If so, execute step 520; otherwise, execute step 516.

[0194] Specifically, when the fault detection device determines that there is no fault continuously and the current output is not from the main filter, the fault detection device may execute step 520 to switch the output to the main filter to improve the positioning accuracy and reliability. When the fault detection device determines that there is not no fault continuously or the current output is not from the main filter, the fault detection device may execute step 516 above to synchronize the filters after the synchronization flag or synchronization period has expired to improve the positioning accuracy and reliability.

[0195] Step 520: The fault detection device switches to the output of the main filter.

[0196] Step 521: The fault detection device determines whether there is continuous isolation failure and the output is not from the main filter. If so, execute step 522; otherwise, execute step 516.

[0197] Specifically, when the fault detection device determines that consecutive isolations fail and the current output is not the main filter output, the fault detection device may execute step 522 to switch the output to the main filter, improving the positioning accuracy and reliability. When the fault detection device determines that it is not consecutive isolation failure or the current output is not the main filter output, the fault detection device may execute the above-mentioned step 516 to synchronize each filter after the synchronization identifier or synchronization period expires, improving the positioning accuracy and reliability.

[0198] Step 522: The fault detection device switches to the main filter output.

[0199] Based on Figure 5 the method shown above, when the fault detection device meets the above Figure 5 shown conditions, it can adopt the Figure 3 shown method to perform fault detection on N measurement values of the navigation system, improving the positioning accuracy and reliability while reducing the processing burden of the fault detection device and the power consumption of the fault detection device.

[0200] The above mainly introduces the solution provided by the embodiments of the present application from the perspective of interaction between devices. It can be understood that in order to implement the above functions, each device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0201] The embodiments of the present application can divide functional modules for each network element according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the embodiments of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0202] In the case of dividing each functional module corresponding to each function, Figure 6 a fault detection device is shown. The fault detection device 60 can be a fault detection device or a chip or system-on-chip in the fault detection device. The fault detection device 60 can be used to execute the functions of the fault detection device involved in the above embodiments. Figure 6The fault detection device 60 shown includes: a receiving module 601, a processing module 602, and a transmitting module 603.

[0203] The receiving module 601 is configured to obtain N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include inertial navigation system (INS) measurement values; the processing module 602 is configured to perform parallel filtering processing on the N measurement values by using a first filter bank, and determine whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; where the first filter bank includes one main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include (N - 1) measurement values; the input parameters of different first filters are at least one different; the processing module 602 is further configured to, if there are faulty measurement values, determine the faulty measurement values among the N measurement values by using a second filter bank; where the second filter bank includes (N - 1) second filters corresponding to each first filter, and the input parameters of each second filter include (N - 2) measurement values among the (N - 1) measurement values corresponding to its own corresponding first filter; the input parameters of different second filters are at least one different.

[0204] Wherein, the specific implementation manner of the fault detection device 60 may refer to Figures 3 to 5 the behavior functions of the fault detection device in the described fault detection method.

[0205] In a possible design, the device further includes a transmitting module 603; the transmitting module 603 is further configured to use the processing result of the first filter whose input parameters do not include faulty measurement values as the output result.

[0206] In a possible design, the processing module 602 is further configured to obtain the covariance matrix corresponding to the first filter whose input parameters do not include faulty measurement values; the processing module 602 is further configured to adjust the state estimation and covariance matrix of the main filter, each first filter except the first filter whose input parameters do not include faulty measurement values, and the second filter whose input parameters include faulty measurement values according to the state estimation and covariance matrix.

[0207] In a possible design, the processing module 602 is further configured to perform preprocessing on the N measurement values; where the preprocessing includes converting the N measurement values to the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value according to the already obtained measurement values.

[0208] In a possible design, the processing module 602 is specifically configured to process N measurement values through a main filter to obtain the processing result of the main filter; process the measurement values through N first filters respectively to obtain the processing result of each first filter; compare the processing result of the main filter with the processing result of each first filter to obtain a first difference corresponding to each first filter; determine whether there is a first difference greater than or equal to a first threshold; if so, determine that there are faulty measurement values.

[0209] In a possible design, the processing module 602 is further specifically configured to: process the measurement values through each second filter to obtain the processing result of each second filter; compare the processing result of each first filter with the processing results of (N - 1) second filters corresponding to the first filter to obtain a second difference corresponding to each second filter; determine whether there is a first filter for which the second differences corresponding to each second filter are all less than a second threshold; if there is, determine the measurement value not included in the input parameters of the first filter as a faulty measurement value.

[0210] In a possible design, the processing module 602 is further configured to process N measurement values through a main filter to obtain the state estimate and covariance matrix corresponding to the main filter; initialize each first filter and each second filter according to the state estimate and covariance matrix.

[0211] In a possible design, the device further includes a sending module 603; the sending module 603 is further configured to use the processing result of the main filter as the output result when there are no faulty measurement values.

[0212] In a possible design, the processing module 602 is further configured to periodically obtain the state estimate and covariance matrix of the main filter; adjust the state estimate and covariance matrix of each first filter and each second filter according to the state estimate and covariance matrix corresponding to the main filter.

[0213] In a possible design, the processing module 602 is further configured to update the main filter, the first filter, and the second filter whose input parameters include measurement values when the measurement values are updated.

[0214] In a possible design, the measurement source includes at least four or more of the following: Global Navigation Satellite System (GNSS) measurement source, Real - Time Kinematic (RTK), Inertial Measurement Unit (IMU), Wheel Speed Sensor (WSS), Laser - Based Localization (LBL), Vector Semantic Localization (VSL).

[0215] As another implementable manner, Figure 6The receiving module 601 and the transmitting module 603 therein may be replaced by a transceiver, and the processing module 602 may be replaced by a processor. The transceiver may integrate the functions of the receiving module 601 and the transmitting module 603, and the processor may integrate the function of the processing module 602. Further, Figure 6 the fault detection device 60 shown may further include a memory. When the receiving module 601 and the transmitting module 603 are replaced by a transceiver and the processing module 602 is replaced by a processor, the fault detection device 60 involved in the embodiments of the present application may be Figure 2 the communication device shown.

[0216] The embodiments of the present application also provide a computer-readable storage medium. All or part of the processes in the above method embodiments may be completed by a computer program instructing relevant hardware. The program may be stored in the above computer-readable storage medium. When the program is executed, it may include the processes of the above method embodiments. The computer-readable storage medium may be an internal storage unit of any of the foregoing embodiments of the terminal (including the data sending end and / or the data receiving end), such as the hard disk or memory of the terminal. The above computer-readable storage medium may also be an external storage device of the above terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the above terminal. Further, the above computer-readable storage medium may also include both the internal storage unit and the external storage device of the above terminal. The above computer-readable storage medium is used to store the above computer program and other programs and data required by the above terminal. The above computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0217] It should be noted that the terms "first" and "second" in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0218] It should be understood that in this application, "at least one (item)" means one or more, "a plurality" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or its similar expression refers to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0219] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and conciseness of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0220] In several embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0221] The unit described as a separated component may or may not be physically separated. The component displayed as a unit may be a physical unit or multiple physical units, that is, it can be located in one place, or it can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0222] In addition, each functional unit in various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0223] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0224] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A fault detection method, characterized in that, Including: Obtaining N measurement values corresponding to M measurement sources; where N is an integer greater than or equal to 4; the N measurement values include inertial navigation system (INS) measurement values. Preprocessing the N measurement values; where the preprocessing includes converting the N measurement values to the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values. Performing parallel filtering processing on the N measurement values using a first filter bank, and determining whether there are faulty measurement values among the N measurement values according to the processing results of each filter included in the first filter bank; if there are faulty measurement values, using a second filter bank to determine the faulty measurement values among the N measurement values; where the first filter bank includes one main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include N - 1 measurement values; at least one of the input parameters of different first filters is different; the second filter bank includes N - 1 second filters corresponding to each first filter, and the input parameters of each second filter include N - 2 measurement values among the N - 1 measurement values of its corresponding first filter; at least one of the input parameters of different second filters is different.

2. The method according to claim 1, characterized in that, The method further includes: Taking the processing result of the first filter whose input parameters do not include the faulty measurement value as the output result.

3. The method according to claim 2, wherein Obtaining the state estimate and covariance matrix corresponding to the first filter whose input parameters do not include the faulty measurement value. Adjusting the state estimate and covariance matrix of the main filter, each first filter except the first filter whose input parameters do not include the faulty measurement value, and the second filter whose input parameters include the faulty measurement value according to the state estimate and the covariance matrix.

4. The method according to any one of claims 1 to 3, characterized in that, The performing parallel filtering processing on the N measurement values using the first filter bank includes: Processing the N measurement values through the main filter to obtain the processing result of the main filter. Processing the measurement values through the N first filters respectively to obtain the processing result of each first filter. Comparing the processing result of the main filter with the processing results of each first filter to obtain the first difference corresponding to each first filter. Judging whether there is a first difference greater than or equal to a first threshold; if so, determining that there are faulty measurement values.

5. The method according to any one of claims 1-3, characterized in that, The determining the faulty measurement values among the N measurement values using the second filter bank includes: Processing the measurement values through each second filter to obtain the processing result of each second filter. Comparing the processing result of each first filter with the processing results of the N - 1 second filters corresponding to the first filter to obtain the second difference corresponding to each second filter. Determine whether there is a first filter for which the second differences corresponding to each of the second filters are all less than a second threshold; if there is, determine the measurement values not included in the input parameters of the first filter as faulty measurement values.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: Processing the N measurement values through the main filter to obtain the state estimate and covariance matrix corresponding to the main filter; initializing each first filter and each second filter according to the state estimate and the covariance matrix.

7. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When there are no faulty measurement values, use the processing result of the main filter as the output result.

8. The method according to claim 7, wherein Periodically obtain the state estimate and covariance matrix of the main filter; Adjust the state estimates and covariance matrices of each first filter and each second filter according to the state estimate and covariance matrix corresponding to the main filter.

9. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When the measurement values are updated, update the main filter, the first filter, and the second filter whose input parameters include the measurement values.

10. The method according to any one of claims 1-3, wherein The measurement sources include at least four or more of the following: Global Navigation Satellite System (GNSS) measurement source, Real-Time Kinematic (RTK), Inertial Measurement Unit (IMU), Wheel Speed Sensor (WSS), Laser-Based Localization (LBL), Vector Semantic Localization (VSL).

11. A fault detection device, characterized in that, Comprises: A receiving module, configured to obtain N measurement values corresponding to M measurement sources; wherein, N is an integer greater than or equal to 4; the N measurement values include Inertial Navigation System (INS) measurement values; A processing module, configured to preprocess the N measurement values; wherein, the preprocessing includes converting the N measurement values into the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that can be measured by the measurement source corresponding to the measurement value; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values; A processing module, configured to perform parallel filtering processing on the N measurement values by using a first filter bank, and determine whether there are faulty measurement values among the N measurement values according to the processing results of the respective filters included in the first filter bank; if there are faulty measurement values, use a second filter bank to determine the faulty measurement values among the N measurement values; wherein, the first filter bank includes one main filter and N first filters; the input parameters of the main filter include the N measurement values; the input parameters of each first filter include N-1 measurement values; the input parameters of different first filters are at least one different; the second filter bank includes N-1 second filters corresponding to each first filter, and the input parameters of each second filter include N-2 measurement values among the N-1 measurement values of its corresponding first filter; the input parameters of different second filters are at least one different.

12. A fault detection device, characterized in that, Comprises: A main filter, N first filters, N-1 second filters corresponding to each first filter, and a processor; wherein the processor is communicatively connected to the main filter, each first filter, and each second filter respectively; N is an integer greater than or equal to 4; The processor is configured to preprocess the N measurement values; wherein the preprocessing includes converting the N measurement values into the same coordinate system and / or deleting abnormal measurement values; the abnormal measurement values include measurement values outside the measurement range and / or measurement values deviating from the measurement trajectory; the measurement range is the range that the measurement source corresponding to the measurement value can measure; the measurement trajectory is the trajectory predicted by the measurement source corresponding to the measurement value based on the already obtained measurement values; The main filter is configured to perform parallel filtering processing on the N measurement values corresponding to M measurement sources to obtain the processing result of the main filter; wherein the input parameters of the main filter include the N measurement values; the N measurement values include inertial navigation system (INS) measurement values; The N first filters are configured to perform parallel filtering processing on N-1 measurement values respectively to obtain the processing result of each first filter; wherein the input parameters of each first filter include N-1 measurement values; at least one of the input parameters of different first filters is different; The N-1 second filters corresponding to each first filter are configured to perform parallel filtering processing on N-2 measurement values respectively to obtain the processing result of each second filter; wherein the input parameters of each second filter include N-2 measurement values among the N-1 measurement values of the first filter corresponding to itself; at least one of the input parameters of different second filters is different; The processor is configured to determine whether there are faulty measurement values among the N measurement values according to the processing result of the main filter and the processing results of the N first filters; if there are faulty measurement values, then use the processing results of the N-1 second filters corresponding to each first filter to determine the faulty measurement values among the N measurement values.

13. A fault detection device, characterized in that, The device includes one or more processors and a transceiver; the one or more processors and the transceiver support the device to execute the fault detection method according to any one of claims 1-10.

14. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores computer instructions or programs, and when the computer instructions or programs run on a computer, the computer is caused to execute the fault detection method according to any one of claims 1-10.

15. A fault detection system, characterized in that, The system includes the fault detection device according to any one of claims 11-13.

16. An autonomous vehicle, characterized in that, The autonomous vehicle includes the fault detection device according to any one of claims 11-13.

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