Fault-tolerant method and device for INS / GNSS loose integrated navigation system and medium

Abstract

The invention discloses a fault tolerance method and device for an INS / GNSS loose integrated navigation system and a medium, and belongs to the technical field of integrated navigation, and the fault tolerance method specifically comprises the following steps: S1, INS data fault mark judgment and normal integrated navigation process starting; s2, calculating a measurement residual error and a residual error covariance matrix; s3, fault judgment based on a chi-square detection algorithm; s4, Kalman filtering measurement updating and INS data correction are carried out under the fault-free condition; and S5, processing under a fault condition. Compared with a traditional fault tolerance method, the method has the outstanding advantages that the fault mode of the INS data is considered, faults of the INS data and the GNSS data can be detected at the same time, the detection accuracy is higher, and the method comprehensively covers the fault detection range of the two kinds of data. The simultaneous detection capability greatly improves the accuracy of fault detection.

Description

Technical Field

[0001] The invention belongs to the technical field of integrated navigation, and in particular relates to a fault-tolerant method, equipment and medium for an INS / GNSS loosely integrated navigation system. Background Art

[0002] The control system has higher and higher requirements for the reliability of the integrated navigation system. In the specific field of INS / GNSS integrated navigation system, the industry has been actively exploring effective fault-tolerant methods to ensure the stable operation of the system. At present, there are two commonly used methods: one is to detect and isolate GNSS faults through specific fault-tolerant means; the other is to directly combine without performing GNSS fault detection.

[0003] For the first method, which is to detect and isolate GNSS faults, most of them are based on the premise that INS is reliable to identify GNSS faults. This method seems reasonable in theory, but it has serious limitations in practical applications. When INS fails due to various complex reasons (such as inertial sensor failure, internal circuit problems, or influence of the external environment), the entire fault detection mechanism will fall into chaos. Since the traditional fault detection method relies on the reliable data of INS to judge the fault of GNSS, it will mistakenly judge the fault of INS as the fault of GNSS. Once this misjudgment occurs, the system will enter the pure inertial navigation mode according to the wrong judgment result. In the pure inertial navigation mode, since the INS itself has failed, its navigation system error will diverge at a very fast speed. It will cause the system combination accuracy to decrease or function to be lost, and may even cause the entire mission to fail.

[0004] As for the method of directly combining without fault detection, although it simplifies the system design and operation process to a certain extent, it brings more serious problems. In this mode, both faulty GNSS data and faulty INS data are very likely to be introduced into the integrated navigation system. Once these faulty data are involved in the navigation calculation process, they will have a great negative impact on the combined accuracy of the entire system. Over time, this impact will continue to intensify, which may cause the combined accuracy of the system to drop to an unacceptable level, and eventually completely lose the function of the integrated navigation system.

[0005] In summary, in the field of INS / GNSS loosely integrated navigation fault-tolerant technology, traditional methods, due to their own defects, are difficult to meet the current high-reliability requirements for simultaneous detection of INS and GNSS faults. This urgently requires us to design a new reliable fault-tolerant method suitable for INS / GNSS loosely integrated navigation systems to meet the high-precision landing and high-reliability requirements of navigation control systems. Summary of the invention

[0006] The purpose of the present invention is to provide a fault-tolerant method, device and medium for an INS / GNSS loosely integrated navigation system, which can reduce the demand for hardware redundancy and reduce product costs on the one hand; on the other hand, it can ensure that when an INS or GNSS fails, a relatively high combined accuracy can be achieved to meet the high-precision landing point and high reliability requirements of the navigation control system, so as to overcome the deficiency of the prior art that it is difficult to meet the high reliability requirements for simultaneously detecting INS and GNSS failures.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: An INS / GNSS loose integrated navigation system fault tolerance method specifically comprises the following steps: S1, judge the INS data fault flag, default INS data is normal, input INS data and GNSS data into the normal integrated navigation process, establish state equation and measurement equation based on INS / GNSS loose integrated navigation Kalman filter principle, obtain INS error filter data, update the state of INS error filter data, and obtain INS error state prediction value and INS error prediction value covariance matrix; S2, calculating the INS error state prediction value, the INS error prediction value covariance matrix, the INS data and the GNSS data to obtain the measurement residual and the residual covariance matrix; S3, using the chi-square detection algorithm to obtain the constructed detection quantity for the measurement residual and the covariance matrix of the residual, and using the constructed detection quantity to perform fault judgment on the INS data and the GNSS data; S4, when it is determined that there is no fault in both the INS data and the GNSS data, the fault time is reset, and the Kalman filter measurement is updated to obtain the error estimation value of the INS data. The error estimation value of the INS data is used to correct the INS data, and the normal integrated navigation process is completed; S5, when it is determined that at least one of the INS data and the GNSS data has a fault, the fault time of the INS data and the GNSS data continuously entering the card-square detection is accumulated, the accumulated fault time is judged, and the fault result is obtained according to the set fault threshold.

[0008] The state update described in S1 includes state equation discretization, filtering state prediction and prediction state covariance matrix calculation.

[0009] The specific calculation formula for the measurement residual in S2 is: ; in, represents the measurement residual, Indicates measurement, represents the measurement matrix, represents the state prediction value; The specific calculation formula of the covariance matrix of the residual in S2 is: ; in represents the measurement residual covariance matrix, represents the state prediction covariance matrix, represents the measurement noise covariance matrix.

[0010] The construction detection quantity is obtained in S3 The chi-square test algorithm calculation formula is: ; in, It obeys the chi-square distribution with n degrees of freedom, that is, , n is the measurement dimension; set up T is the threshold value, if Less than T , then there is no fault in both INS data and GNSS data; if Greater than or equal to T , then at least one of the INS data or the GNSS data is faulty.

[0011] The threshold T For the settings, please refer to the chi-square test confidence table.

[0012] The process of performing Kalman filter measurement update in S4 to obtain the error estimate of INS data is specifically as follows: according to the Kalman filter principle, the Kalman gain is calculated. , through the Kalman gain Calculate error estimates for INS data ; The Kalman gain The specific calculation formula is: ; in represents the state prediction covariance matrix, represents the measurement matrix, represents the measurement residual covariance matrix; The error estimate The specific calculation formula is: ; in, is the INS error state prediction value, is the measurement residual. The error estimate reflects the deviation between the INS data and the true value, which can be used to correct the INS data later.

[0013] In S5, when the accumulated fault time is less than the set fault threshold, it is considered that the GNSS data is faulty, and the GNSS data directly exits the normal integrated navigation process; when the accumulated fault time is greater than or equal to the set fault threshold, it is considered that the INS data is faulty, and the INS data enters the low-precision integrated navigation process.

[0014] After the INS data enters the low-precision integrated navigation process, low-precision integrated navigation settings need to be performed, specifically including: Reset filter: When the accumulated fault time is greater than the set fault threshold and the INS data is judged to be faulty, the filter is first initialized and reset; the setting parameters in the filter P and Q matrices are amplified to reduce the combined weight of the INS data during combination; and the impact of the faulty INS data on the results of the entire navigation system is reduced; INS fault flag enable: When an INS data fault is detected, the INS data fault flag is enabled to indicate that the INS data is in a fault state.

[0015] On the other hand, the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned INS / GNSS loosely integrated navigation system fault tolerance method when executing the computer program.

[0016] In yet another aspect, the present invention further provides a computer-readable storage medium for storing computer instructions, wherein the computer instructions, when executed by a processor, implement the above-mentioned INS / GNSS loosely integrated navigation system fault tolerance method.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides a fault-tolerant method for an INS / GNSS loosely integrated navigation system. Compared with the traditional fault-tolerant method, the outstanding advantage of the present method is that the fault mode of INS data is taken into account, and the faults of INS data and GNSS data can be detected at the same time, with a higher detection accuracy. In actual navigation applications, INS and GNSS data complement each other, but both may be affected by different factors and fail. The present method comprehensively covers the fault detection range of these two types of data. This simultaneous detection capability greatly improves the accuracy of fault detection, and can detect potential problems more timely and accurately than traditional methods, thereby avoiding increased navigation errors or even navigation failures caused by failures not being detected in time. At the same time, the method can take reasonable targeted processing measures according to different fault sources, and the engineering application reliability is higher. The method can meet the fault-tolerant requirements of most low-cost systems for INS / GNSS loosely integrated navigation systems, and provides a solution for low cost and high reliability of integrated navigation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flowchart of a fault-tolerant method for an INS / GNSS loosely integrated navigation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] See also Figure 1 , a fault-tolerant method for an INS / GNSS loosely integrated navigation system, comprising the following steps: S1, INS data fault sign judgment and normal combined navigation process start, judge the INS data fault sign, in the initial state, the default INS data is normal, then the INS data and GNSS data are input into the normal combined navigation process. In this process, work is carried out based on the INS / GNSS loose combined navigation Kalman filter principle. First, establish the state equation and measurement equation, and obtain the INS error filter data by solving and calculating these equations. The state of the INS error filter data is updated. The state update link contains several important sub-steps, including state equation discretization, filter state prediction and state prediction covariance matrix calculation. Through these steps, the INS error state prediction value and the INS error prediction value covariance matrix can be obtained, providing basic data for subsequent calculations and judgments.

[0022] S2: Calculation of measurement residuals and residual covariance matrix. After obtaining the INS filter state prediction value, INS covariance matrix prediction value, INS data and GNSS data, further calculation is required. The calculation of measurement residuals is a key step, and its specific calculation formula is: ; in, represents the measurement residual, Indicates measurement, represents the measurement matrix, Represents the state prediction value; the measurement residual reflects the difference between the measured value and the estimated measured value obtained based on the predicted state; The specific calculation formula of the covariance matrix of the residual in S2 is: ; in represents the measurement residual covariance matrix, represents the state prediction covariance matrix, Represents the measurement noise covariance matrix; the residual covariance matrix is ​​used to measure the uncertainty of the measurement residual.

[0023] S3: Fault judgment based on the chi-square detection algorithm; The chi-square test algorithm is used to obtain the constructed test quantity for the measurement residual and the covariance matrix of the residual. The chi-square test algorithm calculation formula is: ; in, It obeys the chi-square distribution with n degrees of freedom, that is, , n is the measurement dimension; when INS or GNSS fails, the detection quantity is constructed will become larger, so based on the above principle, it can be determined whether the INS or GNSS is faulty.

[0024] set up T is the threshold value, if Less than T , then there is no fault in both INS data and GNSS data; if Greater than or equal to T , then at least one of the INS data or GNSS data is faulty. Threshold T The setting of can refer to the chi-square detection confidence table and determine the appropriate value based on the system's confidence requirements for fault detection.

[0025] S4: Kalman filter measurement update and INS data correction in the absence of faults; When it is determined that there is no fault in both INS data and GNSS data, the fault time is reset to zero and the Kalman filter measurement is updated. In this process, the Kalman gain is first calculated according to the Kalman filter principle. , through the Kalman gain Calculate error estimates for INS data , the error estimate The specific calculation formula is: ; in is the INS error state prediction value, is the measurement residual. This error estimate accurately reflects the deviation between the INS data and the true value. Finally, the INS data is corrected using the error estimate of the INS data, and the normal combined navigation process is completed. In this way, the accuracy of the INS data can be effectively improved, thereby improving the performance of the entire navigation system.

[0026] S5: handling in case of failure; When it is determined that at least one of the INS data and the GNSS data is faulty, the fault time of the INS data and the GNSS data continuously entering the card-square detection is accumulated. In this process, it is necessary to judge the accumulated fault time and obtain the fault result according to the set fault threshold. Specifically, when the accumulated fault time is less than the set fault threshold, the GNSS data is considered to be faulty, and the GNSS data directly exits the normal combined navigation process. When the accumulated fault time is greater than or equal to the set fault threshold, the INS data is considered to be faulty, and the INS data enters the low-precision combined navigation process.

[0027] After the INS data enters the low-precision integrated navigation process, a series of low-precision integrated navigation settings need to be performed. The first is to reset the filter. When an INS data failure is detected, the filter is initialized and reset. The specific operation is to reset all the filter parameters to the initial values, and at the same time, amplify the setting parameters in the filter P and Q matrices to reduce the combined weight of the INS data during the combination, and reduce the impact of the faulty INS data on the results of the entire navigation system; at the same time, the INS data fault flag is enabled, indicating that the INS data is in a faulty state.

[0028] In specific applications, a navigation system is designed for a small aircraft, and the INS / GNSS loosely integrated navigation system fault-tolerant method is adopted. During the flight, the aircraft's INS system and GNSS system continuously collect data. Initially, the system assumes that the INS data is normal, and inputs the INS and GNSS data into the normal integrated navigation process. For example, the position, speed, attitude and other data collected by the INS and the position and speed information provided by the GNSS are used to establish state equations and measurement equations. Through the Kalman filter principle, the state update is first completed to obtain the INS error state prediction value and the state prediction covariance matrix value. Then, the measurement residual and the residual covariance matrix are calculated; then, the chi-square detection algorithm is used to calculate the constructed detection quantity. , assuming that the construction detection quantity calculated this time is If the value is less than the set threshold, it means that both INS data and GNSS data are fault-free, the fault time is reset, and the Kalman filter measurement is updated, including the calculation of the Kalman gain. ， By Kalman gain Calculate the error estimate of INS data; finally correct the INS data to ensure the accuracy of aircraft navigation.

[0029] However, if at some stage of the flight, due to environmental factors or other reasons, the INS or GNSS data becomes abnormal. For example, Greater than or equal to T, then at least one of the INS data or GNSS data is faulty, and the fault time begins to accumulate. If the accumulated fault time is less than the set fault threshold within a period of time, the system determines that the GNSS data is faulty. At this time, the GNSS data exits the normal combined navigation process, and the aircraft temporarily relies on INS data to continue navigation, but this situation will be marked and monitored. If the accumulated fault time exceeds the set fault threshold, it is determined that the INS data is faulty, and the INS data enters the low-precision combined navigation process. After the INS data enters the low-precision combined navigation process, it is necessary to set the low-precision combined navigation, initialize and reset the filter, and amplify the setting parameters in the filter P and Q matrices to reduce the combined weight of the INS data during the combination, and the INS data fault flag is enabled. While reducing the weight of the INS data, the system continues to update the state estimate using the Kalman filter to ensure the reliability of the aircraft navigation as much as possible. At the same time, the system may issue an INS fault alarm to prompt ground maintenance personnel or pilots to pay attention. Through this fault-tolerant method, the aircraft can still maintain a relatively stable and safe flight state when the INS or GNSS data fails.

[0030] An embodiment of the present application also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is operable to enable a computer to execute part or all of the steps of any one of the INS / GNSS loosely integrated navigation system fault-tolerant methods described in the above method embodiments.

[0031] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer program enables the computer to execute part or all of the steps of any one of the INS / GNSS loosely integrated navigation system fault-tolerant methods recorded in the above method embodiments.

[0032] The present application provides an INS / GNSS loosely integrated navigation system fault-tolerance method. Compared with traditional fault-tolerance methods, the outstanding advantage of this method is that it takes into account the failure mode of INS data and can detect failures of INS data and GNSS data at the same time, with higher detection accuracy.

[0033] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0034] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. A fault-tolerant method for INS / GNSS loosely integrated navigation system, characterized in that: The following steps are involved: S1, judge the INS data fault flag, default the INS data to be normal, input the INS data and GNSS data into the normal integrated navigation process, establish the state equation and measurement equation based on the INS / GNSS loose integrated navigation Kalman filter principle, obtain the INS error filter data, update the state of the INS error filter data, and obtain the INS error state prediction value and the INS error prediction value covariance matrix; S2, calculating the INS error state prediction value, the INS error prediction value covariance matrix, the INS data and the GNSS data to obtain the measurement residual and the residual covariance matrix; S3, using the chi-square detection algorithm to obtain the constructed detection quantity for the measurement residual and the covariance matrix of the residual, and using the constructed detection quantity to perform fault judgment on the INS data and the GNSS data; S4, when it is determined that there is no fault in both the INS data and the GNSS data, the fault time is reset, and the Kalman filter measurement is updated to obtain the error estimation value of the INS data. The error estimation value of the INS data is used to correct the INS data, and the normal integrated navigation process is completed; S5, when it is determined that at least one of the INS data and the GNSS data has a fault, the fault time of the INS data and the GNSS data continuously entering the card-square detection is accumulated, the accumulated fault time is judged, and the fault result is obtained according to the set fault threshold.

2. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 1, characterized in that: The state update described in S1 includes state equation discretization, filter state prediction and prediction state covariance matrix calculation.

3. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 1, characterized in that: The specific calculation formula for the measurement residual in S2 is: ； in, represents the measurement residual, Indicates measurement, represents the measurement matrix, represents the state prediction value; The specific calculation formula of the covariance matrix of the residual in S2 is: ； in represents the measurement residual covariance matrix, represents the state prediction covariance matrix, represents the measurement noise covariance matrix.

4. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 3, characterized in that: The construction detection quantity is obtained in S3 The chi-square test algorithm calculation formula is: ； in, It obeys the chi-square distribution with n degrees of freedom, that is, , n is the measurement dimension; set up T is the threshold value, if Less than T , then there is no fault in both INS data and GNSS data; if Greater than or equal to T , then at least one of the INS data or the GNSS data is faulty.

5. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 4, characterized in that: The threshold T For the settings, please refer to the chi-square test confidence table.

6. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 1, characterized in that: The process of performing Kalman filter measurement update in S4 to obtain the error estimate of INS data is specifically as follows: according to the Kalman filter principle, the Kalman gain is calculated. , through the Kalman gain Calculate error estimates for INS data ; The Kalman gain The specific calculation formula is: ； in represents the state prediction covariance matrix, represents the measurement matrix, represents the measurement residual covariance matrix; The error estimate The specific calculation formula is: ； in, is the INS error state prediction value, is the measurement residual.

7. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 1, characterized in that: In S5, when the accumulated fault time is less than the set fault threshold, the GNSS data is considered to be faulty, and the GNSS data directly exits the normal integrated navigation process; When the accumulated fault time is greater than or equal to the set fault threshold, the INS data is considered to be faulty and the INS data enters the low-precision integrated navigation process.

8. The fault-tolerant method of INS / GNSS loosely integrated navigation system according to claim 7, characterized in that: After the INS data enters the low-precision integrated navigation process, low-precision integrated navigation settings need to be performed, specifically including: Reset filter: When INS data failure is detected, the filter is initialized and reset; the setting parameters in the filter P and Q matrices are amplified to reduce the combined weight of INS data when combined; INS fault flag enable: When an INS data fault is detected, the INS data fault flag is enabled.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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