A GNSS slow-varying fault detection method that jointly observes the domain, position domain, and time domain

Abstract

The present invention proposes a GNSS slow-varying fault detection method that combines the observation domain, the position domain, and the time domain, which relates to the field of satellite positioning technology. The method includes: based on the concept of solution separation in the position domain, regarding the difference between the complete set solution and the subset solution as a fault test quantity and storing it in a smoothing window; at the same time, using the residual information in the observation domain to design a consistency check factor to accurately obtain the end time of the slow-varying fault and avoid the false alarm risk caused by abnormal information; in addition, based on the fault test quantity stored in the smoothing window in the time domain, reconstructing and obtaining a new fault test quantity to quickly detect the slow-varying fault; finally, by comparing the size of the fault test quantity with the detection threshold, the detection of the slow-varying fault is completed. This method not only supports the fast and accurate detection of slow-varying faults, but also is applicable to complex dynamic scenarios, providing new ideas for GNSS data quality control.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite positioning, and in particular to a GNSS slowly varying fault detection method that combines the observation domain, position domain, and time domain. Background Art

[0002] With the completion of the network construction of China's Beidou navigation system and the continuous development of the Global Navigation Satellite Systems (GNSS), its positioning, navigation, and timing (PNT) services have been widely used in fields such as smart cities, intelligent transportation, and unmanned driving, and are developing towards a more ubiquitous, intelligent, and trustworthy direction. Due to the inherent vulnerabilities of GNSS, the universality of errors, uncertainties, and the susceptibility of signals to shielding, etc., the PNT services will inevitably be interfered by various signal anomalies or faults. As an abnormal state, if a fault is not successfully detected or identified, it will threaten the navigation service performance of end users. Therefore, improving the fault detection ability is the key to promoting the development of high-precision GNSS position services, and it has important significance and research value.

[0003] Considering that after error correction, there are still slowly growing errors (SGE) of the same order of magnitude as the observation noise in the observations due to reasons such as receiver aging, satellite orbit, and clock anomalies. Given the slow growth trend of slowly varying faults over time, the fault test quantity takes a long time to trigger the detection condition, resulting in a gradual decrease in the accuracy of the positioning solution and seriously affecting the continuity of GNSS navigation services. Therefore, slowly varying faults are regarded as the most dangerous type of faults, and it is necessary to study a robust detection method for slowly varying faults. Summary of the Invention

[0004] In order to overcome the above-mentioned shortcomings of the prior art, the main object of the present invention is a GNSS slowly varying fault detection method that combines the observation domain, position domain, and time domain.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A GNSS slowly varying fault detection method that combines the observation domain, position domain, and time domain, comprising the following steps:

[0006] Construct a fault test quantity T s1 and a fault detection threshold T d1 based on the solution separation in the position domain, and count the count information of each subset;

[0007] Design a consistency test factor F c according to the observation residual; if the consistency test factor F passes the 3Sigma criterion testc If there is no significant difference in the sequence and the lock-loss duration of the subset exceeds the set threshold, the fault test quantity of the subset within the smoothing window is initialized and the current fault test quantity is recorded; if the consistency test factor F c If there are significant differences in the sequence, the fault detection quantity of all subsets in the smoothing window is initialized and the current fault detection quantity is recorded;

[0008] The count of each subset is compared with the size of the smoothing window. If the count of each subset is equal to the size of the smoothing window, the information stored in the smoothing window is used to reconstruct the fault test amount and the detection threshold, and the relationship between the reconstructed fault test amount and the detection threshold is determined; if the count of each subset is not equal to the size of the smoothing window, the relationship between the current fault test amount and the detection threshold is determined; when the fault test amount is less than the detection threshold, the fault location information is obtained according to the detection threshold and the corresponding relationship between the count information of the subset and the location domain.

[0009] It also includes: if the fault verification amount is greater than or equal to a detection threshold, the observation value corresponding to the fault subset is eliminated.

[0010] The fault detection quantity T s1 Solve X for the whole set (0) And the subset solution X (k) The distance between (0) Refers to the positioning solution obtained by taking all satellites into account; Subset solution X (k) It refers to the positioning solution obtained by removing the kth satellite from the solution. Taking subset k as an example, the fault detection quantity T s1 The calculation formula is as follows:

[0011]

[0012] in, is the fault test quantity of the kth subset; k is the subset identifier; q is the direction identifier, and the values of q are 1, 2, and 3, respectively representing the three components of the coordinates in the positioning solution; is the standard deviation of the solution separation information of subset k in direction q; Solve X for the whole set (0) The value of the middle q direction; Solve X for the subset ( k ) The value of the middle q direction;

[0013] The fault detection threshold T d1 The false alarm rate is evenly distributed to the three components of each subset and determined using the probability density function. The formula is as follows:

[0014]

[0015] where x is the integral variable identifier; χ 2 (·) is the probability density function of the chi-square distribution; fr is the degree of freedom; P fa is the false alarm rate; N set is the number of subsets; is the fault detection threshold of subset k.

[0016] The obtaining of the fault test quantity results of the previous and current epochs includes the following steps:

[0017] In the observation domain, the global test quantity T g , which is expressed by the formula as follows:

[0018] T g = r T Wr

[0019] where r is the vector composed of the observation residuals, W is the observation weight matrix, and (·) T represents the vector transpose;

[0020] The consistency test factor F c is the global test quantity at the previous moment divided by the global test quantity at the current moment, and the formula is expressed as follows:

[0021]

[0022] where is the consistency test factor at the j-th moment; j is the time index, and its value is greater than 1; is the global test quantity at the j-th moment; is the global test quantity at the (j - 1)-th moment;

[0023] After continuously tracking for a period of time, store the consistency test factors of each epoch in the cache, and use the 3Sigma criterion to check whether there are significant differences in the fault test quantities of the previous and current epochs.

[0024] The checking for significant differences in the fault test quantity results by the 3Sigma criterion includes the following steps:

[0025] Obtain the mean value of the consistency test factors in the cache and its standard deviation σ F , and the formula is expressed as follows:

[0026]

[0027] where j is the time index; m is the number of consistency test factors F c in the cache;

[0028] If then it indicates that the consistency test factor F at the current moment cThe sample composed of the cache is not satisfied, that is, the 3Sigma criterion is not satisfied. At this time, the current moment is the end moment of the fault.

[0029] Judging the relationship between the reconstructed fault inspection quantity and the detection threshold includes the following steps:

[0030] For the fault inspection quantity T s2 If the count of a certain subset is equal to the size of each subset in the smoothing window, the fault inspection quantity T s1 stored in the smoothing window by this subset can be used to reconstruct the obtained fault inspection quantity T s2 The formula is as follows:

[0031]

[0032] In the formula, k is the subset identifier; j is the count identifier; is the reconstructed fault inspection quantity of subset k; is the fault inspection quantity stored at the j-th position of subset k in the smoothing window; N w is the size of each subset in the smoothing window;

[0033] Fault detection threshold T d2 Calculation of: Assuming that the fault inspection quantities T s1 between epochs are independent of each other. Taking subset k as an example, the threshold T s2 of the reconstructed fault inspection quantity T d2 The formula is as follows:

[0034]

[0035] Among them, x is the integral variable identifier; X 2 (·) is the probability density function of the chi-square distribution; fr is the degree of freedom; P fa is the false alarm rate; N set is the number of subsets; N w is the size of each subset in the smoothing window; is the fault detection threshold of the k-th subset.

[0036] A GNSS slow-varying fault detection system that combines the observation domain, position domain, and time domain includes:

[0037] Decoupling module: Construct the fault inspection quantity T s1 and the fault detection threshold T d1 based on the decoupling in the position domain, and count the count information of each subset;

[0038] Consistency test module: Design the consistency test factor F c according to the observation residual, reflecting the results of the fault inspection quantity between epochs;

[0039] Fault detection and analysis module: If the consistency test factor F is tested by the 3Sigma criterion c If there is no significant difference in the sequence and the lock-loss duration of the subset exceeds the set threshold, the fault test quantity of the subset within the smoothing window is initialized and the current fault test quantity is recorded; if the consistency test factor F c If there are significant differences in the sequence, the fault detection amount of all subsets in the smoothing window is initialized and the current fault detection amount is recorded; then, the count of each subset is compared with the size of the smoothing window. If the count of each subset is equal to the size of the smoothing window, the information stored in the smoothing window is used to reconstruct the fault detection amount and the detection threshold;

[0040] Fault detection result judgment module: if the fault test quantity is greater than or equal to the detection threshold, the observation value corresponding to the fault subset is eliminated; if the fault test quantity is less than the detection threshold, the positioning solution is output according to the detection threshold and the corresponding relationship between the counting information of the subset and the location domain.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: the present application calculates the fault test quantity around the location domain, and reconstructs a new fault test quantity through the information of the smoothing window, which can greatly shorten the detection time of slow-varying faults. In addition, the construction of the consistency check factor can avoid the risk of false alarms caused by the smoothing window. The example results show that compared with the AIME method, the present invention significantly improves the detection performance of slow-varying faults, and effectively alleviates the impact of abnormal observation information on positioning in complex scenes. In short, the present invention not only supports the rapid and accurate detection of slow-varying faults, but also is suitable for complex dynamic scenes, providing new ideas for GNSS data quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0043] Figure 1 It is a schematic diagram of the process structure of the present invention;

[0044] Figure 2 This is a diagram showing the detection effect of the present invention and the classic slow-changing fault detection method AIME under a double fault situation;

[0045] Figure 3 It is a positioning error curve diagram of the present invention in a dynamic urban scene. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0047] The present invention aims to solve the influence of slow-varying faults in GNSS observations on the positioning solution. The positioning technology relied on is standard single point positioning (SPP), and its observation equation is as follows:

[0048] P = ρ + c·(dt r - dt s ) + I + T + ε (1)

[0049] In the formula, P is the pseudorange observation value; ρ is the geometric distance between the receiver position (x r , y r , z r ) and the satellite position (x s , y s , z s ); c is the speed of light; dt r is the receiver clock error; dt s is the satellite clock error; I is the ionospheric error; T is the tropospheric error; ε is other error terms, mainly including observation noise and multipath error caused by the observation environment.

[0050] The satellite position and clock error can be calculated through the GNSS observation file and broadcast ephemeris, and the tropospheric and ionospheric errors can also be corrected using empirical models. Then, by using the Taylor series expansion at the approximate coordinates , the error equation between the receiver and satellite i can be obtained:

[0051]

[0052] In the formula, r i is the observation residual of satellite i; ρ0 is the geometric distance between the satellite position (x si , y si , z si ) and the initial position of the receiver ; dx, dy, and dz respectively represent the correction values of the three coordinate components in the positioning solution, and their coefficients are denoted as lpx i , lpy i , and lpz i ; is the equivalent clock error; l i is the observation vector of satellite i, satisfying l i = P i - ρ i + c·dt s - I i - T i .

[0053] Equation (2) is the error equation of a single satellite. By analogy, the observation equations are constructed from n satellites as shown in the following formula:

[0054]

[0055] The observation equation of formula (3) can be abbreviated as the following matrix expression:

[0056]

[0057] where r is the vector composed of observation residuals; A is the observation matrix; is the correction of the positioning solution; l is the observation vector.

[0058] If the number of visible satellites is greater than the dimension of the correction of the positioning solution, the correction of the positioning solution can be obtained by least squares estimation calculated by the following formula:

[0059]

[0060] where W is the observation weight matrix, which can be simplified to the identity matrix; (·) T represents the transpose of a vector or matrix; (·) -1 represents the inverse operation of a matrix.

[0061] Finally, superimpose it on the approximate coordinates to obtain the final positioning solution X, which can be calculated by the following formula:

[0062]

[0063] Refer to Figure 1 to know that the specific steps include:

[0064] A GNSS slow-varying fault detection method that combines the observation domain, position domain, and time domain, including the following steps:

[0065] Construct a fault test quantity T s1 and a fault detection threshold T d1 based on the solution separation in the position domain, and count the counting information of each subset;

[0066] Design a consistency test factor F c according to the observation residuals; if there is no significant difference in the consistency test factor F c sequence through the 3Sigma criterion test, and the out-of-lock duration of the subset exceeds the set threshold, then initialize the fault test quantity of the subset in the smoothing window and record the current fault test quantity; if there is a significant difference in the consistency test factor F c sequence, then initialize the fault detection quantity of all subsets in the smoothing window and record the current fault test quantity;

[0067] The count of each subset is compared with the size of the smoothing window. If the count of each subset is equal to the size of the smoothing window, the information stored in the smoothing window is used to reconstruct the fault test quantity and the detection threshold, and the relationship between the reconstructed fault test quantity and the detection threshold is determined; if the count of each subset is not equal to the size of the smoothing window, the relationship between the current fault test quantity and the detection threshold is determined; when the fault test quantity is less than the detection threshold, the fault location information is obtained according to the detection threshold and the corresponding relationship between the count information of the subset and the location domain.

[0068] It also includes: when the fault inspection amount is greater than or equal to the detection threshold, eliminating the observation value corresponding to the fault subset until the fault inspection amount is less than the detection threshold.

[0069] Fault detection quantity T s1 Solve X for the whole set (0) and subset solution X (k) The distance between (0) Refers to the positioning solution obtained by involving all satellites in the solution; Subset solution X (k) It refers to the positioning solution obtained by excluding the kth satellite from the solution. Taking subset k as an example, the fault detection quantity T s1 The calculation formula is as follows:

[0070]

[0071] in, is the fault test quantity of the kth subset; k is the subset identifier; q is the direction identifier, and the values of q are 1, 2, and 3, respectively representing the three components of the coordinates in the positioning solution; is the standard deviation of the solution separation information of subset k in direction q; Solve X for the whole set (0) The value of the middle q direction; Solve X for the subset (k) The value of the middle q direction;

[0072] Fault detection threshold T d1 The false alarm rate is evenly distributed to the three components of each subset and determined using the probability density function. The formula is as follows:

[0073]

[0074] Where x is the integral variable identifier; χ 2 (·) is the probability density function of the chi-square distribution; fr is the degree of freedom; P fa is the false alarm rate; N set is the number of subsets; is the fault detection threshold for subset k.

[0075] Obtaining the fault inspection results of the previous and next epochs includes the following steps:

[0076] In the observation domain, the global test quantity T g , which is expressed by the following formula:

[0077] T g = r T Wr

[0078] where r is the vector composed of observation residuals, W is the observation weight matrix, and (·) T represents the vector transpose;

[0079] The consistency test factor F c is the global test quantity at the previous moment divided by the global test quantity at the current moment, which is expressed by the following formula:

[0080]

[0081] where is the consistency test factor at the j-th moment; j is the time index and its value is greater than 1; is the global test quantity at the j-th moment; is the global test quantity at the (j - 1)-th moment;

[0082] After continuous tracking for a period of time, store the consistency test factors of each epoch in the cache, and use the 3Sigma criterion to check whether there are significant differences in the fault test quantities between the previous and current epochs.

[0083] The steps for checking the fault test quantity results by the 3Sigma criterion are as follows:

[0084] Obtain the mean value of the consistency test factors in the cache and its standard deviation σ F , which is expressed by the following formula:

[0085]

[0086]

[0087] where j is the time index; m is the number of the consistency test factors F c in the cache;

[0088] If then it indicates that the consistency test factor F c at the current moment does not satisfy the sample composed of the cache, that is, it does not satisfy the 3Sigma criterion, and at this time, the current moment is the fault end moment.

[0089] Judge the relationship between the reconstructed fault test quantity and the detection threshold, including the following steps:

[0090] For the fault test quantity Ts2 For the construction, if the count of a certain subset is equal to the size of each subset in the smoothing window, the fault detection quantity T stored by the subset in the smoothing window can be utilized s1 to reconstruct and obtain the fault detection quantity T s2 The formula is expressed as follows:

[0091]

[0092] In the formula, k is the subset identifier; j is the count identifier; is the fault detection quantity after reconstruction of subset k; is the fault detection quantity stored at the j-th position of subset k in the smoothing window; N w is the size of each subset in the smoothing window;

[0093] Fault detection threshold T d2 Calculation of: Assume that the fault detection quantities T s1 between epochs are independent. Taking subset k as an example, then the threshold T s2 of the reconstructed fault detection quantity T d2 The formula is expressed as follows:

[0094]

[0095] where x is the integral variable identifier; χ 2 (·) is the probability density function of the chi-square distribution; fr is the degree of freedom; P fa is the false alarm rate; N set is the number of subsets; N w is the size of each subset in the smoothing window; is the fault detection threshold of the k-th subset.

[0096] Example 2

[0097] is the basis of Example 1. When determining the fault location, it includes the following steps:

[0098] Step 1, construct the fault detection quantity and detection threshold around the location domain, and count the count information of each subset. This part introduces the idea of solution separation based on the location domain to construct the fault detection quantity T s1 and its detection threshold T d1 , which mainly includes 2 small steps:

[0099] (1) Construction of the fault detection quantity T s1 Based on the fault detection idea of the location domain, the distance between the global solution and the subset solution is regarded as the fault detection quantity T

[0100] s1 ​, that is, "solution separation". The subsets are constructed based on fault assumptions. Taking the single fault assumption as an example, it is assumed that only 1 satellite is abnormal at the current moment, and the number of subsets is equivalent to the number of satellites. The complete set solution X (0) refers to the positioning solution obtained by using all satellites for calculation, and the subset solution X (k) refers to the positioning solution obtained by using some satellites, such as excluding the k-th satellite, for calculation. The fault test quantity T s1 can be calculated by the following formula:

[0101]

[0102] In the formula, k is the subset identifier; is the fault test quantity of the k-th subset; q is the direction identifier, and its values 1, 2, and 3 respectively represent the three components of the coordinates in the positioning solution; is the value of the q direction in the complete set solution X (0) ; is the value of the q direction in the subset solution X (k) ; is the standard deviation of the solution separation of subset k in the q direction.

[0103] The fault test quantities of each subset can be calculated through formula (7), then stored in the specified position of the smoothing window, and the counting information of each subset is statistically calculated to reflect whether the satellite signal is continuously tracked.

[0104] (2) Calculation of the fault detection threshold T d1

[0105] Assume that the three components of the coordinates in the positioning solution X are independent of each other, and the fault test quantities of each subset then follow the chi-square distribution with 3 degrees of freedom, otherwise follow the non-central chi-square distribution with 3 degrees of freedom. According to the risk requirements of the application scenario, the false alarm rate is evenly distributed to the three components of each subset, and the fault detection threshold of each subset is determined by using the probability density function. It can be calculated by the following formula:

[0106]

[0107] In the formula, x is the integral variable identifier; χ 2 (·) is the probability density function of the chi-square distribution; fr is the degree of freedom; P fa is the false alarm rate; N set is the number of subsets; is the fault detection threshold of subset k.

[0108] ​Step 2: Design a consistency test factor based on the residuals in the observation domain to check whether there are significant differences in the fault test quantities of the previous and current epochs. If there are no significant differences, proceed to Step 3; otherwise, proceed to Step 4. This part introduces the calculation of the consistency check factor around the residuals in the observation domain, mainly including three small steps:

[0109] (1) In the observation domain, the global test quantity T g can be calculated by the following formula:

[0110] T g = r T Wr (9)

[0111] where r is the vector composed of observation residuals, W is the observation weight matrix, and (·) T represents vector transpose.

[0112] (2) The consistency test factor F c is the global test quantity at the previous moment divided by the global test quantity at the current moment and can be calculated by the following formula:

[0113]

[0114] where is the consistency test factor at the j-th moment; j is the time index and its value is greater than 1; is the global test quantity at the j-th moment; is the global test quantity at the (j - 1)-th moment.

[0115] (3) After continuously tracking for a period of time, store the consistency test factors of each epoch in the cache and use the 3Sigma criterion to check whether there are significant differences in the fault test quantities of the previous and current epochs (this difference has no specific quantitative index and depends on the situation). The specific operations are as follows:

[0116] ① Calculate the mean of the consistency test factors in the cache and its standard deviation σ F , which can be calculated by the following formula:

[0117]

[0118] where j is the time index; m is the number of consistency test factors F c in the cache.

[0119] ② If it indicates that the consistency test factor F c at the current moment does not satisfy the sample composed of the cache, then regard the current moment as the fault end moment and proceed to Step 4.

[0120] Step 3, if the out-of-lock duration of each satellite or subset exceeds the tolerance time, then execute Step 5; otherwise, execute Step 6.

[0121] Step 4, initialize the fault detection quantity of all subsets within the smoothing window, and record the current fault detection quantity.

[0122] Step 5, initialize the fault detection quantity of this subset within the smoothing window, and record the current fault detection quantity.

[0123] Step 6, if the count of each subset is equal to the size of the smoothing window, then execute Step 7; otherwise, execute Step 8.

[0124] Step 7, utilize the fault detection quantity T s1 stored by this subset within the smoothing window to reconstruct and obtain the fault detection quantity T s2 and its fault detection threshold T d2 . This part mainly includes two small steps:

[0125] (1) Construction of the fault detection quantity T s2 As known from Step 6, when the count of a certain subset is equal to the size of each subset within the smoothing window, the fault detection quantity T

[0126] stored by this subset within the smoothing window can be utilized to reconstruct and obtain the fault detection quantity T s1 , which can be calculated by the following formula: s2 In the formula, k is the subset identifier; j is the count identifier;

[0127]

[0128] is the reconstructed fault detection quantity of subset k; is the fault detection quantity T stored at the j-th position of subset k within the smoothing window; s1 ; N w is the size of each subset within the smoothing window.

[0129] (2) Calculation of the fault detection threshold T d2 Assume that the fault detection quantities T

[0130] between epochs are independent of each other. Then, the threshold T s1 of the reconstructed fault detection quantity T s2 can be determined by the following formula: d2 In the formula, x is the integral variable identifier; χ

[0131]

[0132] is the probability density function of the chi-square distribution; fr is the degree of freedom; P 2 is the false alarm rate; N fa is the size of each subset within the smoothing window. setis the number of subsets; N w is the size of each subset in the smoothing window; is the fault detection threshold of the k-th subset.

[0133] Step 8, determine the relationship between the current fault test quantity and the detection threshold. If the test quantity is greater than or equal to the detection threshold, then eliminate the observed values corresponding to the fault subset; otherwise, output the positioning solution.

[0134] Figure 2 is the detection effect diagram of the present invention and the classical slow-varying fault detection method, that is, the present invention and the autonomous integrity monitoring extrapolation method AIME, in the case of double faults. The fault simulation period is from 1600 seconds to 1799 seconds; Figure 3 is the positioning error curve diagram in the urban dynamic scenario.

[0135] It should be noted that in the present invention, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0136] The above embodiments are only illustrative examples of the present invention and do not constitute a limitation on the protection scope of the present invention. Any design identical or similar to the present invention falls within the protection scope of the present invention.

Claims

1. A GNSS slow-varying fault detection method that jointly observes the domain, position domain, and time domain, characterized in that, The following steps are involved: Location-domain-based solution separation structure fault test quantity and fault detection threshold , and count information of each subset is statistically analyzed; Design a consistency test factor based on the observation residuals ; if passed Criterion to test the consistency test factor There is no significant difference in the sequence, and the out-of-lock duration of the subset exceeds the set threshold, then initialize the fault test quantity of the subset within the smoothing window and record the current fault test quantity; If the consistency test factor has a significant difference in the sequence, initialize the fault detection amounts of all subsets within the smoothing window and record the current fault test amount; Compare the count of each subset with the size of the smoothing window. If the count of each subset is equal to the size of the smoothing window, use the information stored in the smoothing window to reconstruct the fault detection amount and the detection threshold, and determine the relationship between the reconstructed fault detection amount and the detection threshold. If the counts of each subset are not equal to the size of the smoothing window, the relationship between the current fault inspection amount and the detection threshold is determined; When the fault detection amount is less than a detection threshold, fault location information is obtained according to the detection threshold and in combination with a corresponding relationship between the count information of the subset and the location domain.

2. The GNSS slow - change fault detection method that combines the observation domain, position domain, and time domain as described in claim 1, wherein, Also includes: If the fault verification value is greater than or equal to the detection threshold, the observation values corresponding to the fault subset are eliminated.

3. The GNSS slow-varying fault detection method combining the observation domain, position domain and time domain as claimed in claim 1, characterized in that The fault detection quantity is the distance between the complete set solution and the subset solution. The complete set solution refers to the positioning solution obtained by solving with all satellites participating; the subset solution refers to the positioning solution obtained by solving after removing the th satellite from participating in the solution; taking the subset as an example, the calculation formula of the fault detection quantity is as follows: wherein, is the fault test quantity of the subset; is the subset identifier; is the direction identifier, and the values 1, 2, and 3 respectively represent the three components of the coordinates in the positioning solution; is the standard deviation of the solution separation information of the subset in the direction of ; is the value of the in the direction of the full set solution; is the value of the in the direction of the subset solution; The fault detection threshold It is determined by evenly distributing the false alarm rate to the three components of each subset and using the probability density function, and the formula is as follows: Among them, is the integral variable identifier; is the probability density function of the chi-square distribution; is the degree of freedom; is the false alarm rate; is the number of subsets; is the subset k fault detection threshold.

4. The GNSS slow-varying fault detection method that combines the observation domain, position domain, and time domain as claimed in claim 1, wherein The fault verification amount includes a global verification amount, specifically: In the observation domain, the global test statistic , is expressed by the following formula: Among them, is a vector composed of observation residuals, is the observation weight matrix, represents the vector transpose; Consistency test factor It is the global test value at the previous moment divided by the global test value at the current moment, and the formula is as follows: Among them, is the consistency check factor at the moment; is the time index, and its value is greater than 1; is the global test quantity at the moment; is the global test quantity at the moment; After continuous tracking for a period of time, store the consistency test factors of each epoch in the cache, and use the criterion to check whether there is a significant difference in the fault test quantities of the previous and subsequent epochs.

5. The GNSS slow-varying fault detection method that combines the observation domain, position domain, and time domain as claimed in claim 4, wherein The above-mentioned by The criterion is used to test that there are significant differences in the results of the fault test quantity, including the following steps: Obtain the mean of the consistency check factors in the cache and its standard deviation , which is expressed by the formula as follows: Among them, is the time index; is the number of consistency check factors in the cache; If , it indicates that the consistency check factor at the current moment does not satisfy the sample composed of the cache, that is, it does not satisfy the criterion, and at this time, the current moment is the end moment of the fault.

6. The GNSS slow-varying fault detection method that combines the observation domain, position domain, and time domain as claimed in claim 1, wherein The determining of the relationship between the reconstructed fault detection amount and the detection threshold comprises the following steps: For the construction of the fault detection quantity if the count of a certain subset is equal to the size of each subset in the smoothing window, the fault detection quantity stored in the smoothing window by this subset can be used to reconstruct and obtain the fault detection quantity The formula is as follows: In the formula, is the subset identifier; is the counting identifier; is the subset reconstructed fault test quantity; is the subset the fault test quantity stored at the th position within the smoothing window; is the size of each subset within the smoothing window; Fault detection threshold Calculation: Assume that the fault test quantities between epochs are independent of each other. Taking the subset as an example, the threshold of the reconstructed fault test quantity is expressed by the following formula: Among them, is the integral variable identifier; is the probability density function of the chi-square distribution; is the degree of freedom; is the false alarm rate; is the number of subsets; is the size of each subset in the smoothing window; is the fault detection threshold of the subset.

7. A GNSS slow-varying fault detection system that jointly observes the domain, position domain, and time domain, characterized in that, include: Decoupling module: Construct fault detection variables based on the decoupling of the position domain and the fault detection threshold , and count the count information of each subset; Consistency test module: Design a consistency test factor according to the observation residual , reflecting the results of the fault test quantity in the previous and current epochs; Fault detection and analysis module: If through The criterion test consistency test factor There is no significant difference in the sequence, and the out-of-lock duration of the subset exceeds the set threshold, then initialize the fault test quantity of the subset within the smoothing window and record the current fault test quantity; If the consistency test factor shows significant differences in the sequence, initialize the fault detection quantities of all subsets within the smoothing window and record the current fault test quantity; then, compare the count of each subset with the size of the smoothing window. If the count of each subset is equal to the size of the smoothing window, use the information stored in the smoothing window to reconstruct the fault test quantity and the detection threshold; Fault detection result judgment module: if the fault inspection amount is greater than or equal to the detection threshold, the observation value corresponding to the fault subset is eliminated; The fault verification amount is less than a detection threshold, and a positioning solution is output according to the detection threshold and in combination with the corresponding relationship between the counting information of the subset and the location domain.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that, The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 6 is implemented.

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