Fault Diagnosis and Isolation Method of SINS / Multi-LDV Integrated Navigation System

Abstract

The present invention discloses a fault diagnosis and isolation method for a SINS / multi-LDV combined navigation system, comprising the following steps: constructing a Kalman filter system for combined navigation; when the speed meter measurement data of a new moment arrives, calculating the statistics for fault diagnosis between two sets of speed meters; determining whether the speed meter fails according to the statistics and the confidence threshold and according to the threshold voting rule, and locating the speed meter with the failure when the failure exists; eliminating the measurement data of the speed meter with the failure, and using the remaining measurement data to perform Kalman filter measurement update. The present invention is applied to the fields of combined navigation and fault detection technology, effectively overcoming the defects and shortcomings of the traditional fault detection algorithm that cannot effectively take into account both hard faults and soft faults, improving the reliability and robustness of the SINS / LDV combined navigation system, and having high application value and promotion potential.

Description

Technical Field

[0001] The invention relates to the technical field of combined navigation and fault detection, and relates to information fusion and navigation solution of multiple sensors, and in particular to a fault diagnosis and isolation method of a SINS / multi-LDV combined navigation system. Background Art

[0002] Integrated navigation improves the positioning accuracy and robustness of the navigation system by fusing the inertial navigation system with multiple external auxiliary sensors. Currently, commonly used auxiliary sensors at home and abroad include the Global Navigation Satellite System (GNSS), geomagnetic navigation system, visual odometer and odometer. They provide the position and velocity parameters of the carrier as observation quantities for optimization filtering algorithms such as Kalman filtering, thereby obtaining more accurate integrated navigation results.

[0003] Laser Doppler Velocimeter (LDV) is a new type of fully autonomous speed sensor that can output the real-time speed of the carrier. It has many advantages such as high measurement accuracy, fast dynamic response, wide speed measurement range, good noise characteristics and non-contact measurement. It has been widely used in the field of integrated navigation and is expected to be further developed.

[0004] However, due to its own working characteristics, the laser Doppler velocimeter is prone to signal loss and output speed distortion when passing through waterlogged, muddy and sunken roads. In addition, affected by factors such as refractive index fluctuations in the air, temperature changes, and Doppler frequency estimation errors, the output value of the velocimeter will drift slowly compared to the true value. These defects have greatly limited the accuracy and robustness of the SINS / LDV integrated navigation system.

[0005] At present, the algorithms commonly used in the field of fault diagnosis mainly include the residual chi-square detection algorithm and the sequential probability ratio detection algorithm. Among them, the residual chi-square detection algorithm uses the residual information in the Kalman filtering process to construct the corresponding fault detection function, and determines the fault detection threshold according to the set confidence level, so as to detect the occurrence of the fault. This method has a good detection effect on sudden hard faults, such as speed drop, zero drop and other fault conditions. It can quickly sense the occurrence of faults and isolate them. However, for slowly changing soft faults, the residual chi-square detection algorithm is difficult to perform instant and rapid diagnosis. In the early stage of a slow-changing fault, the fault amplitude is small and difficult to detect. The faulty sensor will continue to pollute the centralized filter, causing the filter output to track the fault information, resulting in residual If the level is always below the confidence threshold, it cannot be effectively detected even if the fault value has accumulated to a large level.

[0006] The sequential probability ratio detection algorithm has a good detection effect on slowly varying faults, but it needs to use the observation data of the previous time to construct the fault detection function, so there is a certain period of false alarm after the fault ends. In addition, there are many other fault diagnosis schemes based on these two methods, but most of them have the problem of difficulty in balancing false alarms and missed alarms.

[0007] Therefore, it is necessary to propose a reliable fault diagnosis and isolation scheme for various hard and soft faults that may occur during the use of the laser Doppler velocimeter, so as to improve the robustness, reliability and accuracy of the SINS / LDV integrated navigation system. Summary of the invention

[0008] In view of the deficiencies in the above-mentioned prior art, the present invention provides a fault diagnosis and isolation method for a SINS / multi-LDV combined navigation system based on the measurement values ​​of multiple laser Doppler velocimeters, which effectively overcomes the defects and shortcomings of traditional fault detection algorithms that cannot effectively take into account both hard faults and soft faults, improves the reliability and robustness of the SINS / LDV combined navigation system, and has high application value and promotion potential.

[0009] To achieve the above object, the present invention provides a fault diagnosis and isolation method for a SINS / multi-LDV integrated navigation system, comprising the following steps:

[0010] Step 1, constructing a Kalman filter system for integrated navigation;

[0011] Step 2, when the speed meter measurement data at a new time arrives, calculate the statistics used for fault diagnosis between the two sets of speed meters;

[0012] Step 3, determining whether the tachometer fails according to the statistic and the confidence threshold and according to the threshold voting rule, and locating the failed tachometer if a fault exists;

[0013] Step 4: Remove the measurement data of the faulty speed meter and use the remaining measurement data to perform Kalman filter measurement update.

[0014] In one embodiment, in step 2, the calculation of the statistics for fault diagnosis between the two sets of tachometers is specifically:

[0015] The proportional factors of the measured values ​​output by the two sets of tachometers are compensated and then subtracted to obtain the measured fault diagnosis variables;

[0016] Statistics for fault diagnosis between the two sets of speed meters are obtained based on the measured fault diagnosis variables and the inertial navigation speed calculated at the current moment.

[0017] In one embodiment, the measured fault diagnosis variable is specifically:

[0018] ;

[0019] in, For speedometer With speedometer The measured fault diagnosis variables, For speedometer The measured value of For speedometer The scale factor of the installation, For speedometer The measured value of For speedometer The scale factor of the installation.

[0020] In one embodiment, the statistic is specifically:

[0021] ;

[0022] in, For speedometer With speedometer Statistics used for fault diagnosis between is the inertial navigation velocity calculated at the current moment, is the speed measurement accuracy of the speedometer.

[0023] In one embodiment, in step 3, three sets of speed meters are used as a speed meter combination, and a threshold voting rule is used to determine whether a speed meter in the speed meter combination fails, and if a failure occurs, the failed speed meter is located.

[0024] In one embodiment, the method of determining whether a tachometer in the tachometer combination fails according to a threshold voting rule and locating the failed tachometer when a failure occurs is specifically as follows:

[0025] The three sets of speedometers in the speedometer combination are defined as speedometer 1, speedometer 2, and speedometer 3;

[0026] Calculate the statistics between tachometer 1 and tachometer 2 for fault diagnosis , Statistics for fault diagnosis between speed meter 2 and speed meter 3 , and the statistics used for fault diagnosis between tachometer 1 and tachometer 3 ;

[0027] Let the confidence threshold be :

[0028] when Time Record Otherwise, remember ;

[0029] when Time Record Otherwise, remember ;

[0030] when Time Record Otherwise, remember ;

[0031] remember :

[0032] when When the three speed meters in the speed meter combination are all operating normally without any faults;

[0033] when and When , it is determined that the tachometer 1 and tachometer 2 in the tachometer combination are not faulty, and the tachometer 3 is faulty;

[0034] when and When , it is determined that the tachometer 2 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 1 is faulty;

[0035] when and When , it is determined that the tachometer 1 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 2 is faulty;

[0036] when When , it is determined that all three speed meters in the speed meter combination are faulty.

[0037] In one embodiment, when the number of speedometers in the integrated navigation system is four or more, the number of the speedometer combinations is multiple, and each of the speedometer combinations covers all the speedometers in the integrated navigation system;

[0038] When the speedometer When both exist in Speedometer Combination 1 and Speedometer Combination 2, if the speedometer If the tachometer combination 1 is judged to be faulty and the tachometer combination 2 is judged to be normal, the tachometer combination 1 is judged to be faulty. No trouble.

[0039] Compared with the prior art, the present invention has the following beneficial technical effects:

[0040] 1. The present invention combines redundant information of multiple sets of laser Doppler velocimeters with laser strapdown inertial units to calculate statistics for fault diagnosis, which can effectively detect various faults of the velocimeters during the integrated navigation process, and accurately locate and isolate the faults when they occur, giving full play to the advantages of high speed measurement accuracy and fast response of the laser Doppler velocimeter, which is conducive to improving the positioning accuracy of the integrated navigation system;

[0041] 2. The present invention has good applicability and robustness, and can detect the constant drift of low fault amplitude in the speed meter during navigation, and can effectively reduce the false alarm rate, missed detection rate and detection delay of slowly varying faults;

[0042] 3. The present invention improves the fault tolerance of the SINS / LDV integrated navigation system. When a certain set of speed meters in the integrated navigation system loses signal or fails, the information of the remaining speed meters can be used to provide observations to ensure the overall positioning accuracy of the integrated navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0044] Figure 1 Flow chart of a fault diagnosis and isolation method for a SINS / multi-LDV integrated navigation system in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the installation relationship between multiple sets of laser Doppler velocimeters and an inertial measurement unit in an example of an embodiment of the present invention;

[0046] Figure 3 1 is a speed curve diagram of a speed meter in an example of an embodiment of the present invention, wherein: (a) is a speed curve diagram of a first speed meter, (b) is a speed curve diagram of a second speed meter, and (c) is a speed curve diagram of a third speed meter;

[0047] Figure 4 A logical diagram of fault diagnosis and isolation in an example of an embodiment of the present invention;

[0048] Figure 5 Schematic diagram of fault detection results in an example of an embodiment of the present invention, wherein: (a) is a diagram of fault detection values ​​of speed meters 1 and 2, (b) is a diagram of fault detection values ​​of speed meters 2 and 3, (c) is a diagram of fault detection values ​​of speed meters 1 and 3, and (d) is a diagram of faulty speed meter position detection.

[0049] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0051] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] This embodiment discloses a fault diagnosis and isolation method for a SINS / multi-LDV combined navigation system. Based on a fault diagnosis strategy of first detecting the occurrence of a fault and then locating the fault, various soft and hard faults that may occur in a speedometer sensor are detected and isolated, thereby overcoming the defects and shortcomings of traditional fault detection algorithms that cannot effectively take into account both hard and soft faults.

[0053] The hardware of the integrated navigation system consists of any multiple sets of laser Doppler velocimeters (LDV) and inertial measurement units (IMU), which can be loaded on any vehicle-type mobile carrier with navigation carrying capacity. In actual installation, the installation distance between LDV and IMU should be as small as possible to minimize the influence of the arm error, mainly considering the forward and right arm errors.

[0054] refer to Figure 1 In this embodiment, the fault diagnosis and isolation method of the SINS / multi-LDV integrated navigation system specifically includes the following steps:

[0055] Step 1, constructing a Kalman filter system for integrated navigation;

[0056] Step 2, when the speed meter measurement data at a new time arrives, calculate the statistics used for fault diagnosis between the two sets of speed meters;

[0057] Step 3: Determine whether the tachometer fails based on the statistic and the confidence threshold according to the threshold voting rule, and locate the failed tachometer if there is a fault;

[0058] Step 4: Remove the measurement data of the faulty speed meter and use the remaining measurement data to perform Kalman filter measurement update.

[0059] The actual speed of the carrier (centered on the inertial group) is recorded as , the speed measurement value output by the tachometer is recorded as . Analyze the measured speed value output by the speed meter, the factors that affect the accuracy are:

[0060] 1. Installation error: When the laser Doppler velocimeter is installed on the carrier, it is limited by the installation process level. The velocimeter coordinate system and the inertial coordinate system are difficult to completely coincide, and there will be a difference between the installation inclination angle and the set inclination angle. These factors will introduce a proportional factor error. , Installation angle error and lever arm error ;

[0061] 2. Measurement noise: The laser Doppler velocimeter has good noise characteristics. Its noise model can be regarded as a mixed Gaussian distribution model (GMM), which consists of many zero-mean sub-distributions. The standard deviation of each sub-distribution is related to the measured speed and the accuracy of the velocimeter. related.

[0062] In this embodiment, the statistics used for fault diagnosis between the two sets of tachometers are calculated, specifically:

[0063] The proportional factors of the measured values ​​output by the two sets of tachometers are compensated and then subtracted to obtain the measured fault diagnosis variables;

[0064] Based on the measured fault diagnosis variables and the inertial navigation speed calculated at the current moment, the statistics used for fault diagnosis between the two sets of speed meters are obtained.

[0065] In practical applications, the lever arm value between the tachometer and the inertial group can be controlled to be very small. Therefore, the influence of the arm error can be approximately ignored, and the installation angle error does not affect the modulus of the speed measured by the speed meter. Based on the above analysis, the speed measurement model of the speed meter can be expressed as:

[0066] ;

[0067] in, is the scale factor for the tachometer installation, The speed measurement accuracy of the tachometer can be obtained by prior calibration; is standard Gaussian white noise.

[0068] For two sets of independent working speed meters , Speedometer , based on the above measurement speed model, we have:

[0069] ;

[0070] ;

[0071] Assuming a speedometer , Speedometer If they are of the same model, then it can be considered that the speedometer , Speedometer have the same precision and standard deviation, i.e. , After compensating the proportional factors of the measured values ​​output by the two sets of tachometers, the measured fault diagnosis variable can be obtained:

[0072] ;

[0073] in, For speedometer With speedometer The measured fault diagnosis variable. and When both are fixed values, The value of can be described by the Gaussian distribution model, that is, .

[0074] However, for actual carrier operation, It is not a fixed value, but will change continuously. The value of cannot be simply described by a Gaussian distribution model. For a navigation process, The value of can be described by the Gaussian mixture distribution model (GMM), and its probability distribution is:

[0075] ;

[0076] in, For each sub-distribution (corresponding to different speed values ) occurs in the mixed model, are the means and covariances of each sub-distribution. Usually, each sub-distribution does not appear with equal probability in the overall distribution model, and it is difficult to accurately estimate and predict it. Therefore, a certain distribution description is used. It is very difficult, and we should adopt the idea of ​​dynamic change, that is, to establish a dynamically changing distribution according to the real-time speed of the carrier.

[0077] In the integrated navigation system, the velocity calculated by the inertial group is recorded as When the integrated navigation system performs real-time filtering and correction on the error, The actual speed of the carrier The error is very small. At the same time, considering the high level of measurement accuracy of the tachometer, Therefore, this embodiment uses real-time replace Construct a real-time changing statistical distribution model. The standard deviation change caused by the speed error is The magnitude can be ignored. After standard normalization and averaging, we can get the speedometer With speedometer Statistics for fault diagnosis ,for:

[0078] ;

[0079] ;

[0080] It obeys the chi-square distribution with 1 degree of freedom. Therefore, the corresponding confidence threshold can be determined by selecting an appropriate confidence level to detect the occurrence of faults.

[0081] when When the value is higher than the threshold, that is, when the speedometer is detected to be faulty, it is necessary to further locate the location of the fault, so as to eliminate the erroneous measurement value and avoid its contamination of the integrated navigation system. In view of the defects of the traditional residual detection method, based on the threshold voting idea, redundant fault diagnosis is performed by using multiple sets of speedometers to determine the faulty sensor. In this embodiment, three sets of speedometers are used as a speedometer combination, and the threshold voting rule is used to determine whether the speedometer in the speedometer combination is faulty, and the faulty speedometer is located when a fault exists. The specific implementation process is as follows:

[0082] First, the three sets of speedometers in the speedometer combination are defined as speedometer 1, speedometer 2, and speedometer 3;

[0083] Secondly, calculate the statistics for fault diagnosis between tachometer 1 and tachometer 2 , Statistics for fault diagnosis between speed meter 2 and speed meter 3 , and the statistics used for fault diagnosis between tachometer 1 and tachometer 3 ;

[0084] Then, let the confidence threshold be , and then do the following:

[0085] when Time Record Otherwise, remember ;

[0086] when Time Record Otherwise, remember ;

[0087] when Time Record Otherwise, remember ;

[0088] Finally, remember , and then make the following judgment:

[0089] when When the three speed meters in the speed meter combination are all operating normally without any faults;

[0090] when and When , it is determined that the tachometer 1 and tachometer 2 in the tachometer combination are not faulty, and the tachometer 3 is faulty;

[0091] when and When , it is determined that the tachometer 2 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 1 is faulty;

[0092] when and When , it is determined that the tachometer 1 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 2 is faulty;

[0093] when , at least two of the three speedometers in the speedometer combination are faulty, and it is impossible to distinguish them at this time, so it is determined that all three speedometers in the speedometer combination are faulty, and no corresponding measurement update is performed. Since the probability of two speedometers failing at the same time is very low in actual situations, eliminating all observations has little impact on the overall navigation accuracy.

[0094] In the specific implementation process, when the number of speedometers in the integrated navigation system is more than four, the number of speedometer combinations is multiple, and the grouping basis is that the speedometer combination is as small as possible, and each speedometer combination covers all speedometers in the integrated navigation system. Then, for each speedometer combination and the above threshold voting rules, redundant fault diagnosis and location of the speedometer are performed. It is worth noting that when the speedometer When both exist in Speedometer Combination 1 and Speedometer Combination 2, if the speedometer In the speed meter combination 1, due to the above If the situation is judged as a fault, and the speed meter combination 2 is judged as no fault, the speed meter combination is finally judged as No trouble.

[0095] by Figure 2 The SINS / three-LDV integrated navigation system shown is used as an example to further illustrate the fault diagnosis and isolation method in this embodiment.

[0096] In this example, the navigation process lasts for 2562 seconds, and the first 600 seconds are in a static state for initial alignment; from 850s to 880s of the navigation process, a speed drop hard fault is injected into the first speedometer; from 2050s to 2100s of the navigation process, a linear slow-changing fault is injected into the second speedometer; from 1700s to 1720s of the navigation process, a speed drop hard-changing fault is injected into the third speedometer; from 2200s to 2300s of the navigation process, a speed drop hard fault is injected into the first speedometer and the second speedometer, respectively. The speed curves of the three speedometers are shown in the figure. Figure 3 The scale factor, noise standard deviation, and accuracy of the three sets of velocimeters were obtained through prior calibration, and the confidence level was .

[0097] refer to Figure 4 The fault diagnosis and isolation method in this example specifically includes the following steps:

[0098] Step 1: According to the number of fusion sensors required for integrated navigation, a Kalman filter system for integrated navigation is constructed, including state equations, state vectors, etc. The relevant equations of the Kalman filter system can be expressed as:

[0099] ;

[0100] In the formula, For the current moment. is the state variable, is the strapdown inertial navigation solution error, a total of 15 states, is the attitude solution error, is the speed solution error, is the position solution error, and are the measurement errors of the gyroscope and accelerometer, respectively. , are the system state equation and measurement matrix respectively, and are the known system structure parameters. and are the system noise vector and the measurement noise vector respectively.

[0101] Step 2: When the speedometer measurement data of the new time arrives, the speedometers are grouped together to calculate the statistics used for fault diagnosis. , , , and calculate the corresponding confidence threshold according to the confidence level , expressed as:

[0102] ;

[0103] .

[0104] Step 3: Based on the calculated statistics , , , and the confidence threshold ,According to the threshold voting rule, it is determined whether a fault occurs, and when a fault exists, the faulty tachometer is located;

[0105] Step 4: perform sequential filtering to remove the measurement data of the faulty tachometer obtained through positioning, and use the remaining measurement data to perform Kalman filter measurement update.

[0106] refer to Figure 5 is a schematic diagram of fault detection results, where Figure 5 (a) is the fault detection value diagram of speed meter 1 and 2. Figure 5 (b) is the fault detection value diagram of speed meter 2,3. Figure 5 (c) is the fault detection value diagram of speed meter 1,3. Figure 5 (d) is the faulty speed meter position detection diagram, Figure 5 In (d), the vertical coordinates 1, 2, and 3 respectively represent that speed meters 1, 2, and 3 are faulty, 0 represents no fault, and 4 represents that at least two speed meters are faulty. Figure 5 As shown, the corresponding fault is detected within 10 seconds after the fault occurs at the latest, and when the fault ends, the statistic quickly drops below the threshold, and no delayed false alarm occurs. It can be seen from the diagram marking the sensor fault position that the method of this embodiment can accurately locate the faulty sensor.

[0107] In summary, this embodiment aims at the fault diagnosis problem of laser Doppler velocimeter in the integrated navigation system, and proposes a fault diagnosis and fault tolerance method for the SINS / multi-LDV integrated navigation system based on the construction of multi-laser Doppler velocimeter measurement values. In the integrated navigation process, various faults occurring in the velocimeter can be effectively detected, and can be accurately located and isolated when the fault occurs. This method can effectively improve the positioning accuracy and robustness level of the SINS / LDV integrated navigation system, and has high application value and promotion potential.

[0108] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A fault diagnosis and isolation method for a SINS / multi-LDV integrated navigation system, characterized in that: The steps include: Step 1, constructing a Kalman filter system for integrated navigation; Step 2, when the speed meter measurement data at a new time arrives, calculate the statistics used for fault diagnosis between the two sets of speed meters; Step 3, determining whether the tachometer fails according to the statistic and the confidence threshold and according to the threshold voting rule, and locating the failed tachometer if a fault exists; Step 4, discard the faulty speed meter measurement data, and use the remaining measurement data to perform Kalman filter measurement update; In step 2, the statistics for fault diagnosis between the two sets of tachometers are calculated, specifically: After compensating the proportional factors of the measured values ​​output by the two sets of tachometers, the measured fault diagnosis variables are obtained: in, For speedometer With speedometer The measured fault diagnosis variables, For speedometer The measured value of For speedometer The scale factor of the installation, For speedometer The measured value of For speedometer The scale factor of the installation; The statistics used for fault diagnosis between the two sets of speed meters are calculated based on the measured fault diagnosis variables and the inertial navigation speed calculated at the current moment, which are: in, For speedometer With speedometer Statistics used for fault diagnosis between is the inertial navigation velocity calculated at the current moment, is the speed measurement accuracy of the speedometer.

2. The fault diagnosis and isolation method of the SINS / multi-LDV integrated navigation system according to claim 1, characterized in that: In step 3, three sets of speed meters are used as a speed meter combination, and a threshold voting rule is used to determine whether a speed meter in the speed meter combination fails, and the failed speed meter is located when a failure occurs.

3. The fault diagnosis and isolation method of the SINS / multi-LDV integrated navigation system according to claim 2 is characterized in that: The method of determining whether a speed meter in the speed meter combination fails according to the threshold voting rule and locating the failed speed meter when a failure occurs is specifically as follows: The three sets of speedometers in the speedometer combination are defined as speedometer 1, speedometer 2, and speedometer 3; Calculate the statistics between tachometer 1 and tachometer 2 for fault diagnosis , Statistics for fault diagnosis between speed meter 2 and speed meter 3 , and the statistics used for fault diagnosis between tachometer 1 and tachometer 3 ; Let the confidence threshold be : when Time Record Otherwise, remember ; when Time Record Otherwise, remember ; when Time Record Otherwise, remember ; remember : when When the three speed meters in the speed meter combination are all operating normally without any faults; when and When , it is determined that the tachometer 1 and tachometer 2 in the tachometer combination are not faulty, and the tachometer 3 is faulty; when and When , it is determined that the tachometer 2 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 1 is faulty; when and When , it is determined that the tachometer 1 and tachometer 3 in the tachometer combination are not faulty, and the tachometer 2 is faulty; when When , it is determined that all three speed meters in the speed meter combination are faulty.

4. The fault diagnosis and isolation method of the SINS / multi-LDV integrated navigation system according to claim 2 is characterized in that: When the number of speedometers in the integrated navigation system is four or more, the number of the speedometer combinations is multiple, and each of the speedometer combinations covers all the speedometers in the integrated navigation system; When the speedometer When both exist in Speedometer Combination 1 and Speedometer Combination 2, if the speedometer If the tachometer combination 1 is judged to be faulty and the tachometer combination 2 is judged to be normal, the tachometer combination 1 is judged to be faulty. No trouble.

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