GNSS / INS Positioning System Fault Handling Method and System

Abstract

The present invention relates to the field of satellite navigation technology, and provides a GNSS / INS positioning system fault handling method and system, including the following steps: constructing a GNSS / INS positioning system model; judging whether GNSS observation data is normal data based on GNSS board card information; lateral jump fault detection based on a fuzzy membership function; judging whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold; updating the initial measurement noise to an adaptive measurement noise to obtain positioning information. By adopting multi-level screening and judgment and combining a fuzzy membership function for fault detection, the detection accuracy rate for small lateral jump faults can be greatly improved, and the reliability and accuracy of the positioning result can be enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation, and particularly to a method and system for processing faults in a GNSS / INS positioning system. Background Art

[0002] GNSS (Global Navigation Satellite System) is increasingly widely used in various fields. However, during the GNSS navigation process, it is often subject to various interferences and occlusions, resulting in faults and poor usability in complex environments such as cities and mountains.

[0003] Therefore, INS (Inertial Navigation System) is used to assist GNSS. The original observation information obtained by GNSS is filtered and fused with the measurement values output by INS through filtering to obtain an optimal system state estimator, so as to output more reliable positioning information. When the two navigation devices are combined, the GNSS / INS navigation and positioning system has better integrity, that is, it can still provide positioning information under interference and occlusion conditions.

[0004] However, the existing GNSS / INS navigation and positioning systems only have good detection effects on large lateral jump faults, and have insufficient detection capabilities for small lateral jump faults and slow-varying faults. Furthermore, it has a negative impact on the processing of small lateral jump and slow-varying faults, and is prone to missed detection or false screening of faults in the navigation and positioning system, thereby reducing the reliability and accuracy of the positioning results. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method and system for processing faults in a GNSS / INS positioning system, which can accurately screen, identify, and process the faults that occur during the observation process of the positioning system. The present invention aims to solve the technical problems in the prior art that are prone to missed detection and false detection of small lateral jumps, resulting in insufficient positioning ability and integrity of the positioning system.

[0006] To achieve the above purpose, the present invention is implemented by the following technical solutions:

[0007] A method for processing faults in a GNSS / INS positioning system, comprising the following steps:

[0008] Construct a GNSS / INS positioning system model, where the GNSS / INS positioning system model includes a state equation and a measurement equation, and the measurement equation includes an initial measurement noise;

[0009] Obtain GNSS observation data during the GNSS observation process, and based on the GNSS board information in the GNSS observation data, determine whether the GNSS observation data is normal data;

[0010] If the GNSS observation data is normal data, perform lateral jump fault detection based on the fuzzy membership function on the GNSS observation data to obtain a fault probability, and determine whether there is a fault in the GNSS observation process according to the fault probability;

[0011] If there is a fault in the GNSS observation process, determine whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold;

[0012] If the fault in the GNSS observation process is a small lateral jump fault, update the initial measurement noise to adaptive measurement noise, and update the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise to obtain positioning information.

[0013] Furthermore, the state equation and the measurement equation are respectively:

[0014]

[0015]

[0016] Among them, represents the state vector of the GNSS / INS positioning system at time represents the state transition matrix at time represents the state vector of the GNSS / INS positioning system at time represents the system noise distribution matrix at time represents the system noise at time represents the measurement vector of the GNSS / INS positioning system at time represents the measurement matrix, represents the initial measurement noise at time

[0017] Furthermore, the step of determining whether the GNSS observation data is normal data based on the GNSS board information in the GNSS observation data includes:

[0018] Extracting the number of satellites searched by the main antenna from the GNSS board information, and determining whether the number of satellites searched by the main antenna is within a search quantity limit range;

[0019] If the number of satellites searched by the main antenna is within the search quantity limit range, extracting the satellite solution method from the GNSS board information to determine whether the satellite solution method is a fixed solution;

[0020] If the satellite solution mode is a fixed solution, extracting a solution accuracy characterization value from the GNSS board information, and determining whether the solution accuracy characterization value is within a solution accuracy characterization value threshold;

[0021] If the solution accuracy characterization value is within the solution accuracy characterization value threshold, the GNSS observation data is judged to be normal data.

[0022] Furthermore, after the step of obtaining GNSS observation data in the self-GNSS observation process and determining whether the GNSS observation data is normal data based on the GNSS board information in the GNSS observation data, the method further includes:

[0023] If the GNSS observation data is abnormal data, the GNSS observation data is discarded.

[0024] Furthermore, the step of performing lateral jump fault detection based on a fuzzy membership function on the GNSS observation data to obtain a fault probability, and judging whether there is a fault in the GNSS observation process according to the fault probability includes:

[0025] Extracting the longitude and latitude difference between two consecutive GNSS measurement positions, the meridian curvature radius and the meridian curvature radius of the position of the measured carrier from the GNSS observation data;

[0026] Calculate the lateral displacement and direction difference of two consecutive GNSS measurements based on the longitude and latitude differences, the meridian curvature radius, and the meridian curvature radius;

[0027] The fault probability is calculated based on the lateral displacement and the direction difference. If the fault probability is greater than or equal to 0.3, it is determined that there is a fault in the GNSS observation process. If the fault probability is less than 0.3, it is determined that there is no fault in the GNSS observation process.

[0028] Furthermore, the formula for the failure probability is:

[0029]

[0030] in, represents the probability of failure, represents the lateral displacement, represents the zero deviation mean of the lateral displacement, represents the standard deviation of the lateral displacement, Indicates the direction difference, represents the zero-biased mean of the directional differences, represents the standard deviation of the direction difference, Represents an exponential function.

[0031] Furthermore, after the step of performing lateral jump fault detection based on a fuzzy membership function on the GNSS observation data to obtain a fault probability if the GNSS observation data is normal data, and judging whether there is a fault in the GNSS observation process according to the fault probability, the method further includes:

[0032] If there is no fault in the GNSS observation process, a standard Kalman filter is performed to update the GNSS / INS positioning system model to obtain positioning information.

[0033] Furthermore, the step of judging whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold comprises:

[0034] If the fault probability is less than or equal to the jump threshold, it is determined that the fault in the GNSS observation process is a small lateral jump fault;

[0035] If the failure probability is greater than the jump threshold, the failure of the GNSS observation process is determined to be a large lateral jump failure, and the GNSS observation data is discarded.

[0036] Furthermore, the step of updating the initial measurement noise to an adaptive measurement noise and updating the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise includes:

[0037] Obtaining a time window of the GNSS observation process, and calculating an updated measurement noise based on the time window, the initial measurement noise, a transposed matrix of the initial measurement noise, and the failure probability, wherein the sum of the initial measurement noise and the updated measurement noise is the adaptive measurement noise;

[0038] The initial measurement noise in the measurement equation in the GNSS / INS positioning system model is replaced by the adaptive measurement noise to obtain an adaptive GNSS / INS positioning system model.

[0039] A GNSS / INS positioning system fault processing system is applied to the GNSS / INS positioning system fault processing method as described in the above technical solution, and the system comprises:

[0040] A building module for building a GNSS / INS positioning system model, the GNSS / INS positioning system model including a state equation and a measurement equation, the measurement equation including an initial measurement noise;

[0041] A board card module for obtaining GNSS observation data during the GNSS observation process and determining whether the GNSS observation data is normal data based on the GNSS board card information in the GNSS observation data;

[0042] A fault detection module for, if the GNSS observation data is normal data, performing lateral jump fault detection on the GNSS observation data based on a fuzzy membership function to obtain a fault probability and determining whether there is a fault in the GNSS observation process according to the fault probability;

[0043] A judgment module for, if there is a fault in the GNSS observation process, determining whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and a lateral jump threshold;

[0044] An adaptive module for, if the fault in the GNSS observation process is a small lateral jump fault, updating the initial measurement noise to an adaptive measurement noise and updating the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise to obtain positioning information.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: Through multi-level screening and judgment, it is possible to prevent missed detection caused by slow initial change of lateral jump resulting in delayed information change, calculate the fault probability, and more accurately and meticulously identify the jump fault and jump fault type in GNSS observation, avoiding the situation of missed detection and false detection easily occurring in the traditional method where the identification of faults only uses a single threshold for judgment; for large lateral jump faults, perform measurement update of the standard Kalman filter system, and for small lateral jump faults, perform measurement update after adaptively adjusting the noise parameters, which is beneficial to making the fusion filtering result of GNSS measurement values and INS measurement values better, making the positioning result of the GNSS / INS positioning system more reliable and having higher integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the GNSS / INS positioning system fault processing method in an embodiment of the present invention;

[0047] Figure 2 It is a structural block diagram of the GNSS / INS positioning system fault processing system in another embodiment of the present invention;

[0048] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. Detailed implementation manners

[0049] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0050] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0052] Please refer to Figure 1 , the GNSS / INS positioning system fault handling method in the embodiments of the present invention includes the following steps:

[0053] Step S10: Construct a GNSS / INS positioning system model, where the GNSS / INS positioning system model includes a state equation and a measurement equation, and the measurement equation includes an initial measurement noise;

[0054] Preferably, the GNSS / INS positioning system model realizes optimal state estimation through Kalman filtering. The state vector to be estimated includes attitude angle error, velocity error, position error, gyroscope zero bias and accelerometer zero bias. The state equation is established based on the INS system error state equation. In the INS system error state equation, the product of the previous moment state and the system matrix, and the sum of the product and the system noise matrix is the current moment state. The measurement equation is a GNSS measurement equation, which is constructed based on the position, velocity and heading angle of the main antenna pointing to the secondary antenna at the position of the GNSS main antenna in the navigation system.

[0055] In the step S10, the state equation and the measurement equation are respectively:

[0056]

[0057]

[0058] Among them, represents the state vector of the GNSS / INS positioning system at moment, represents the state transition matrix at moment, represents the state vector of the GNSS / INS positioning system at represents the system noise distribution matrix at represents the system noise at represents the GNSS / INS positioning system at moment, represents the measurement matrix, represents the initial measurement noise at

[0059] Preferably, the state equation is in discrete form, the state transition matrix is calculated in combination with the sampling interval, and the system noise and the initial measurement noise are uncorrelated zero-mean Gaussian white noises.

[0060] Step S20: Obtain GNSS observation data from the GNSS observation process, and judge whether the GNSS observation data is normal data based on the GNSS board information in the GNSS observation data;

[0061] Fusing and filtering the abnormal GNSS observation data with the output measurement value of the INS will result in a large positioning error. Therefore, preliminary screening through the GNSS board information is beneficial to quickly eliminate some abnormal data.

[0062] The step S20 includes:

[0063] S210: Extract the number of satellites searched by the main antenna from the GNSS board information, and judge whether the number of satellites searched by the main antenna is within the satellite search number limit range;

[0064] S220: If the number of satellites searched by the main antenna is within the satellite search number limit range, extract the satellite solution method from the GNSS board information, and judge whether the satellite solution method is a fixed solution;

[0065] S230: If the satellite solution method is a fixed solution, extract the solution accuracy characterization quantity from the GNSS board information, and judge whether the solution accuracy characterization quantity is within the solution accuracy characterization quantity threshold;

[0066] S240: If the solution accuracy characterization quantity is within the solution accuracy characterization quantity threshold, then it is determined that the GNSS observation data is normal data.

[0067] Understandably, in S210 - S240, a preliminary screening of the data accuracy of the GNSS observation data is performed. Among them, the number of satellites searched by the main antenna affects the positioning effect of the device. The fixed solution is a high-precision result obtained by resolving the integer ambiguity of the carrier phase observation value. The solution accuracy characterization quantity is used to measure the solution accuracy, and the observation data meeting a certain accuracy can be used to calculate the positioning result. Specifically, in the satellite search quantity limit range in this embodiment, 10 is the lowest value, and the solution accuracy characterization quantity threshold is 0.2m.

[0068] After the step S20, it further includes:

[0069] S250: If the GNSS observation data is abnormal data, then the GNSS observation data is excluded.

[0070] Preferably, in the initial stage of the lateral jump, the number of satellites searched and the solution state change slowly, with a certain delay, resulting in a risk of missed detection when only using the GNSS board information for abnormal screening. Therefore, after quickly excluding a part of the abnormal data through the preliminary screening using the GNSS board information, the remaining data is then judged for the lateral jump fault.

[0071] Step S30: If the GNSS observation data is normal data, then perform lateral jump fault detection on the GNSS observation data based on the fuzzy membership function to obtain a fault probability, and judge whether there is a fault in the GNSS observation process according to the fault probability;

[0072] The INS system can provide a relatively reliable state estimation result for a period of time when the GNSS fails. Therefore, combining the heading estimation result of the INS system, using the characteristics that the lateral speed during vehicle driving is generally small and the driving heading cannot change rapidly, the lateral displacement and direction change are calculated, and the fault detection is carried out in combination with the calculation result.

[0073] The step S30 includes:

[0074] S310: Extract the longitude and latitude difference of the positions of two consecutive GNSS measurements from the GNSS observation data, the radius of curvature of the meridian circle at the position of the measured carrier, and the radius of curvature of the prime vertical circle;

[0075] S320: Based on the longitude and latitude difference, the radius of curvature of the meridian circle, and the radius of curvature of the prime vertical circle, calculate the lateral displacement and direction difference of two consecutive GNSS measurements;

[0076] Due to the particularity of the measurement error caused by lateral obstacles in the positioning result, when a lateral jump occurs, between two consecutive GNSS measurements of the position of the measured carrier, the lateral displacement and the driving direction of the measured carrier along the driving direction will undergo sudden changes. Preferably, when calculating the lateral displacement, the vehicle heading angle and the height of the vehicle are also required for calculation, while when calculating the direction difference, it is not necessary to calculate in combination with the vehicle heading angle.

[0077] S330: Calculate the failure probability based on the lateral displacement and the direction difference. If the failure probability is greater than or equal to 0.3, it is determined that there is a failure in the GNSS observation process. If the failure probability is less than 0.3, it is determined that there is no failure in the GNSS observation process.

[0078] In the step S30, the formula for the failure probability is:

[0079]

[0080] where represents the failure probability, represents the lateral displacement, represents the zero-bias mean value of the lateral displacement, represents the standard deviation of the lateral displacement, represents the direction difference, represents the zero-bias mean value of the direction difference, represents the standard deviation of the direction difference, represents the exponential function.

[0081] Preferably, in the working conditions where the lateral acceleration or the yaw rate is too large, it will have a great negative impact on driving safety. Therefore, only normal driving conditions are considered. In normal driving conditions, the lateral acceleration of the vehicle does not exceed 4 m / s², and the yaw rate does not exceed 0.4 rad / s. Therefore, in this embodiment, the standard deviation of the lateral displacement is specifically set to 0.8, and the standard deviation of the direction difference is specifically set to 5. The zero-bias refers to the non-zero signal output by the gyroscope in the stationary state. In the working conditions of stable driving, in this embodiment, the zero-bias mean values of both the lateral displacement and the direction difference are set to 0.

[0082] After the step S30, it further includes:

[0083] S340: If there is no failure in the GNSS observation process, perform standard Kalman filtering to update the GNSS / INS positioning system model to obtain positioning information.

[0084] For S310~S340, the greater the lateral jump error is, that is, the greater both the lateral displacement and the direction difference are, the greater the probability of additional noise and faults in the measurement. The fault probability is calculated based on a fuzzy membership function, and the fuzzy membership function is established based on the basic formula of the normal distribution function. Understandably, the fault probability conforms to the trend of the normal distribution, increasing slowly first, then growing rapidly, and finally slowly approaching the maximum value. When the lateral jump error is very small, the fault probability is low, and even if there is a certain rate of missed detections, it will not affect the positioning system. When the lateral jump error increases, corresponding to the stage where the ordinate of the normal distribution rises rapidly, that is, the fault probability rises rapidly with the increase of the lateral jump error, and it is very sensitive to the detection of faults. When the lateral jump error is relatively large, that is, both the lateral displacement and the direction difference are relatively large, the growth rate of the fault probability tends to level off, and it is more conservative and accurate when confirming the fault as a large lateral jump and excluding the data.

[0085] Step S40: If there is a fault in the GNSS observation process, determine whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold;

[0086] In the traditional method, only a single threshold is used to judge the fault situation. When the change difference between two consecutive measurements is greater than the threshold, it is judged as a fault condition. If the single threshold is set too low, it will lead to an excessive false detection rate. If the single threshold is set too high, it will lead to a large missed detection rate, and it cannot accurately reflect the environmental conditions of lateral occlusion. In this embodiment, the fuzzy membership function is used to calculate the fault probability, which weakens the problem of over-reliance on the threshold size for Boolean logic judgment relying on a single threshold. By setting an interval threshold for the fault probability, it can not only judge whether there is a jump fault in the observation process, but also prompt the possible small lateral jump error.

[0087] The step S40 includes:

[0088] S410: If the fault probability is less than or equal to the jump threshold, determine that the fault in the GNSS observation process is a small lateral jump fault;

[0089] S420: If the fault probability is greater than the jump threshold, determine that the fault in the GNSS observation process is a large lateral jump fault, and exclude the GNSS observation data.

[0090] Preferably, in S410~S420, according to the 3sigma principle, the jump threshold is set to 0.7. When the value of the fault probability exceeds 0.3 but does not exceed 0.7, it is judged that there is a small lateral jump fault, and the observation data corresponding to the small lateral jump fault does not need to be excluded, and the fusion filtering accuracy can still be guaranteed after the adaptive adjustment of the noise parameters.

[0091] Step S50: If the fault in the GNSS observation process is a small lateral jump fault, update the initial measurement noise to an adaptive measurement noise, and update the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise to obtain positioning information.

[0092] Preferably, the small lateral jump fault brings additional noise. The Sage-Husa adaptive Kalman filter algorithm can be used to adaptively update the initial measurement noise, which is beneficial to greatly improving the accuracy of the positioning result and does not eliminate the observation data corresponding to the small lateral jump, which is beneficial to improving the integrity of the positioning system and can still output the positioning result under certain occlusion or interference.

[0093] The step S50 includes:

[0094] S510: Obtain the time window of the GNSS observation process, and calculate the updated measurement noise based on the time window, the initial measurement noise, the transpose matrix of the initial measurement noise, and the fault probability. The sum of the initial measurement noise and the updated measurement noise is the adaptive measurement noise;

[0095] S520: Replace the initial measurement noise in the measurement equation in the GNSS / INS positioning system model with the adaptive measurement noise to obtain an adaptive GNSS / INS positioning system model.

[0096] Preferably, in S510-S520, the initial measurement noise is relatively accurate and reliable under most working conditions. The diagonal elements in the matrix of the updated measurement noise can be restricted according to the initial measurement noise and the allowable scaling factor of the GNSS measurement noise parameters to improve the reliability of the adaptive filtering. The allowable scaling factor of the GNSS measurement noise parameters is less than 1.

[0097] Please refer to Figure 2 , in the GNSS / INS positioning system fault handling system provided in another embodiment of the present invention, which is applied to the GNSS / INS positioning system fault handling method as described in the above embodiment, the system includes:

[0098] A construction module 10 for constructing a GNSS / INS positioning system model, where the GNSS / INS positioning system model includes a state equation and a measurement equation, and the measurement equation includes an initial measurement noise;

[0099] In the construction module 10, the state equation and the measurement equation are respectively:

[0100]

[0101]

[0102] Among them, represents the state vector of the GNSS / INS positioning system at time represents the state transition matrix at time represents the state vector of the GNSS / INS positioning system at time represents the system noise distribution matrix at time represents the system noise at time represents the state vector of the GNSS / INS positioning system at time represents the measurement matrix represents the initial measurement noise at time

[0103] The board module 20 is used to obtain GNSS observation data during the GNSS observation process, and based on the GNSS board information in the GNSS observation data, determine whether the GNSS observation data is normal data;

[0104] The board module 20 includes:

[0105] The first unit is used to extract the number of satellites searched by the main antenna from the GNSS board information, and determine whether the number of satellites searched by the main antenna is within the satellite search quantity limit range;

[0106] The second unit is used to, if the number of satellites searched by the main antenna is within the satellite search quantity limit range, extract the satellite solution method from the GNSS board information, and determine whether the satellite solution method is a fixed solution;

[0107] The third unit is used to, if the satellite solution method is a fixed solution, extract the solution accuracy characterization quantity from the GNSS board information, and determine whether the solution accuracy characterization quantity is within the solution accuracy characterization quantity threshold;

[0108] The fourth unit is used to, if the solution accuracy characterization quantity is within the solution accuracy characterization quantity threshold, determine that the GNSS observation data is normal data.

[0109] The fifth unit is used to, if the GNSS observation data is abnormal data, eliminate the GNSS observation data.

[0110] The fault detection module 30 is configured to, if the GNSS observation data is normal data, perform lateral jump fault detection on the GNSS observation data based on a fuzzy membership function to obtain a fault probability, and determine whether there is a fault in the GNSS observation process according to the fault probability;

[0111] The fault detection module 30 includes:

[0112] The sixth unit is configured to extract the longitude and latitude difference between two consecutive GNSS measurements of the position from the GNSS observation data, the radius of curvature of the meridian circle at the position where the measured carrier is located, and the radius of curvature of the prime vertical circle;

[0113] The seventh unit is configured to calculate the lateral displacement and direction difference between two consecutive GNSS measurements based on the longitude and latitude difference, the radius of curvature of the meridian circle, and the radius of curvature of the prime vertical circle;

[0114] The eighth unit is configured to calculate a fault probability based on the lateral displacement and the direction difference. If the fault probability is greater than or equal to 0.3, it is determined that there is a fault in the GNSS observation process. If the fault probability is less than 0.3, it is determined that there is no fault in the GNSS observation process.

[0115] The formula for the fault probability is:

[0116]

[0117] Wherein, represents the fault probability, represents the lateral displacement, represents the zero bias mean of the lateral displacement, represents the standard deviation of the lateral displacement, represents the direction difference, represents the zero bias mean of the direction difference, represents the standard deviation of the direction difference, represents the exponential function.

[0118] The ninth unit is configured to, if there is no fault in the GNSS observation process, perform standard Kalman filtering to update the GNSS / INS positioning system model to obtain positioning information.

[0119] The judgment module 40 is configured to, if there is a fault in the GNSS observation process, determine whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold;

[0120] The judgment module 40 includes:

[0121] The tenth unit is configured to, if the fault probability is less than or equal to the jump threshold, determine that the fault in the GNSS observation process is a small lateral jump fault;

[0122] The eleventh unit is configured to determine that a fault in the GNSS observation process is a large lateral jump fault if the fault probability is greater than the jump threshold, and reject the GNSS observation data.

[0123] The adaptive module 50 is configured to update the initial measurement noise to an adaptive measurement noise if the fault in the GNSS observation process is a small lateral jump fault, and update the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise to obtain positioning information.

[0124] The adaptive module 50 includes:

[0125] The twelfth unit is configured to obtain a time window of the GNSS observation process, and calculate an updated measurement noise based on the time window, the initial measurement noise, the transpose matrix of the initial measurement noise, and the fault probability, and the sum of the initial measurement noise and the updated measurement noise is the adaptive measurement noise;

[0126] The thirteenth unit is configured to replace the initial measurement noise in the measurement equation in the GNSS / INS positioning system model with the adaptive measurement noise to obtain an adaptive GNSS / INS positioning system model.

[0127] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above embodiments merely represent several implementation manners of the present invention, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for handling faults of a GNSS / INS positioning system, characterized in that, The steps include: Constructing a GNSS / INS positioning system model, wherein the GNSS / INS positioning system model includes a state equation and a measurement equation, wherein the measurement equation includes an initial measurement noise; Acquire GNSS observation data from the GNSS observation process, and determine whether the GNSS observation data is normal data based on GNSS board information in the GNSS observation data; If the GNSS observation data is normal data, performing lateral jump fault detection based on a fuzzy membership function on the GNSS observation data to obtain a fault probability, and judging whether there is a fault in the GNSS observation process according to the fault probability; The formula for the failure probability is: Among them, represents the failure probability, represents the lateral displacement, represents the zero-offset mean of the lateral displacement, represents the standard deviation of the lateral displacement, represents the direction difference, represents the zero-offset mean of the direction difference, represents the standard deviation of the direction difference, represents the exponential function; If there is a fault in the GNSS observation process, determining whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold; If the fault in the GNSS observation process is a small lateral jump fault, the initial measurement noise is updated to an adaptive measurement noise, and the GNSS / INS positioning system model is updated to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise to obtain positioning information.

2. The GNSS / INS positioning system fault handling method according to claim 1, characterized in that The state equation and the measurement equation are respectively: Among them, represents the state vector of the GNSS / INS positioning system at time represents the state transition matrix at time represents the state vector of the GNSS / INS positioning system at represents the system noise distribution matrix at represents the system noise at represents the measurement vector of the GNSS / INS positioning system at time represents the measurement matrix represents the initial measurement noise at 3. The GNSS / INS positioning system fault handling method according to claim 1, wherein The step of judging whether the GNSS observation data is normal data based on the GNSS board information in the GNSS observation data comprises: Extracting the number of satellites searched by the main antenna from the GNSS board information, and determining whether the number of satellites searched by the main antenna is within a limit range of the number of satellites searched; If the number of satellites searched by the main antenna is within the search quantity limit range, extracting the satellite solution method from the GNSS board information to determine whether the satellite solution method is a fixed solution; If the satellite solution mode is a fixed solution, extracting a solution accuracy characterization value from the GNSS board information, and determining whether the solution accuracy characterization value is within a solution accuracy characterization value threshold; If the solution accuracy characterization value is within the solution accuracy characterization value threshold, the GNSS observation data is judged to be normal data.

4. The GNSS / INS positioning system fault handling method according to claim 1, characterized in that After the step of acquiring GNSS observation data in the self-GNSS observation process and judging whether the GNSS observation data is normal data based on the GNSS board information in the GNSS observation data, the method further includes: If the GNSS observation data is abnormal data, the GNSS observation data is discarded.

5. The GNSS / INS positioning system fault handling method according to claim 1, wherein The step of performing lateral jump fault detection based on a fuzzy membership function on the GNSS observation data to obtain a fault probability, and judging whether there is a fault in the GNSS observation process according to the fault probability includes: Extracting the longitude and latitude difference between two consecutive GNSS measurement positions, the meridian curvature radius and the meridian curvature radius of the position of the measured carrier from the GNSS observation data; Calculate the lateral displacement and direction difference of two consecutive GNSS measurements based on the longitude and latitude differences, the meridian curvature radius, and the meridian curvature radius; Calculate the fault probability based on the lateral displacement and the direction difference. If the fault probability is greater than or equal to 0.3, it is determined that there is a fault in the GNSS observation process. If the fault probability is less than 0.3, it is determined that there is no fault in the GNSS observation process.

6. The GNSS / INS positioning system fault handling method according to claim 1, characterized in that, After the step of, if the GNSS observation data is normal data, performing lateral jump fault detection on the GNSS observation data based on the fuzzy membership function to obtain the fault probability, and determining whether there is a fault in the GNSS observation process according to the fault probability, further includes: If there is no fault in the GNSS observation process, perform standard Kalman filtering to update the GNSS / INS positioning system model to obtain positioning information.

7. The GNSS / INS positioning system fault handling method according to claim 1, characterized in that The step of determining whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold includes: If the fault probability is less than or equal to the jump threshold, it is determined that the fault in the GNSS observation process is a small lateral jump fault; If the fault probability is greater than the jump threshold, it is determined that the fault in the GNSS observation process is a large lateral jump fault, and the GNSS observation data is excluded.

8. The GNSS / INS positioning system fault handling method according to claim 1, wherein The step of updating the initial measurement noise to the adaptive measurement noise and updating the GNSS / INS positioning system model to the adaptive GNSS / INS positioning system model based on the adaptive measurement noise includes: Obtain the time window of the GNSS observation process, and calculate the updated measurement noise based on the time window, the initial measurement noise, the transpose matrix of the initial measurement noise, and the fault probability. The sum of the initial measurement noise and the updated measurement noise is the adaptive measurement noise; Replace the initial measurement noise in the measurement equation in the GNSS / INS positioning system model with the adaptive measurement noise to obtain the adaptive GNSS / INS positioning system model.

9. A GNSS / INS positioning system fault handling system, which is applied to the GNSS / INS positioning system fault handling method described in any one of the above claims 1 to 8, and is characterized in that, The system includes: A construction module for constructing a GNSS / INS positioning system model, where the GNSS / INS positioning system model includes a state equation and a measurement equation, and the measurement equation includes an initial measurement noise; A board card module for obtaining GNSS observation data from the GNSS observation process and determining whether the GNSS observation data is normal data based on the GNSS board card information in the GNSS observation data; A fault detection module for, if the GNSS observation data is normal data, performing lateral jump fault detection on the GNSS observation data based on the fuzzy membership function to obtain the fault probability, and determining whether there is a fault in the GNSS observation process according to the fault probability; The formula for the fault probability is: Among them, represents the failure probability, represents the lateral displacement, represents the zero-offset mean of the lateral displacement, represents the standard deviation of the lateral displacement, represents the direction difference, represents the zero-offset mean of the direction difference, represents the standard deviation of the direction difference, represents the exponential function; A judgment module for, if there is a fault in the GNSS observation process, determining whether the fault in the GNSS observation process is a small lateral jump fault according to the fault probability and the lateral jump threshold; An adaptive module, which is configured to update the initial measurement noise to adaptive measurement noise if the fault in the GNSS observation process is a small lateral jump fault, and update the GNSS / INS positioning system model to an adaptive GNSS / INS positioning system model based on the adaptive measurement noise, so as to obtain positioning information.

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