Receiver autonomous integrity monitoring method for GNSS integrated positioning performance evaluation

Abstract

The present invention relates to a receiver autonomous integrity monitoring method for evaluating GNSS comprehensive positioning performance, comprising: randomly selecting and constructing a minimum sample set from the observation values ​​of all GNSS satellites of at least one GNSS system in the current epoch; using the minimum sample set to solve the reference position solution vector of the receiver; calculating the post-positioning pseudo-range residual value of each GNSS satellite corresponding to the selected observation value according to the reference position solution vector; calculating the spatial precision factor of the current epoch; using the post-positioning pseudo-range residual value to obtain a consistency set under the minimum sample set; using the post-positioning pseudo-range residual value and the spatial precision factor to obtain an evaluation function of the minimum sample set, and calculating the evaluation function value under the minimum sample set; re-randomly selecting a new minimum sample subset, and repeating the above steps until an optimal solution is obtained; using the obtained optimal consistency set to obtain a receiver position solution vector of the receiver. The present invention has high positioning performance.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning, and in particular to a receiver autonomous integrity monitoring method for evaluating GNSS comprehensive positioning performance. Background Art

[0002] At present, satellite positioning has fully entered the era of multiple global navigation satellite systems (Global Navigation Satellite System, GNSS). The world's four core GNSS constellations (China's Beidou, the United States' GPS, Russia's GLONASS, and Europe's Galileo) provide a total of more than one hundred navigation satellites. Users around the world can receive signals from more than ten or even more than thirty GNSS satellites at any time. Therefore, the status quo of satellite navigation positioning has changed a lot. In many urban positioning scenarios, the main problem faced by users is no longer the inability to obtain a sufficient number of visible satellites, but to correctly detect and exclude those satellites that are severely affected by errors such as non-line of sight (NLOS) and multi-path interference to ensure a stable and reliable positioning result.

[0003] Therefore, the receiver autonomous integrity monitoring algorithm (RAIM) has become one of the standard features of modern receiver terminals, including many consumer-grade terminals. The concept of the RAIM algorithm was first created and developed in the field of civil aviation. The purpose is to autonomously monitor the health of navigation satellite signals and promptly warn users when abnormalities or failures occur. Its basic principle is to check the consistency of the measurement values, that is, the position solution produced by the measurement values ​​that are severely contaminated by NLOS and multipath is less consistent than that of the "clean" satellite measurement values ​​with line of sight (LOS). In other words, the position solution obtained using the correct measurement values ​​is more consistent than the position solution obtained using the measurement values ​​affected by NLOS and multipath errors. Therefore, the measurement values ​​can be evaluated and screened accordingly. By eliminating the faulty measurement values, a more accurate and reliable position solution result can be obtained.

[0004] However, in complex application environments such as cities, GNSS users are seriously affected by errors such as NLOS and multipath due to obstructions from objects such as high-rise buildings, overpasses, and trees. Therefore, consistency detection algorithms such as RAIM and RANSAC have become one of the standard functions of current navigation receiver equipment. Among them, the RAIM algorithm is based on the assumption of single fault conditions and originated in the civil aviation field. It is currently the most widely used consistency detection algorithm. The RANSAC algorithm is based on the assumption of multiple fault conditions and has been widely used in the field of computer vision. In recent years, it has also begun to be applied by researchers and engineers to the field of GNSS navigation and positioning. Compared with RAIM, the RANSAC method can extract the "outside points" from the measurement data with multiple "outside points" and usually has better performance in complex occlusion environments, but the amount of calculation is large.

[0005] However, the positioning accuracy of GNSS does not actually depend entirely on the satellite pseudorange measurement error, but is also closely related to the size of the satellite constellation's Position Dilution of Precision (PDOP). Therefore, if we only consider the influence of the pseudorange measurement error factor in consistency detection, the PDOP value of the constellation may deteriorate due to the deletion of satellites in certain specific spatial orientations, and thus the highest overall positioning accuracy may not necessarily be obtained. In other words, a group of satellites densely distributed near the same spatial orientation does not necessarily achieve higher positioning accuracy than a group of satellites with a dispersed distribution (better PDOP) but slightly larger pseudorange measurement errors. Summary of the invention

[0006] The present invention aims to provide a receiver autonomous integrity monitoring method for evaluating GNSS comprehensive positioning performance.

[0007] To achieve the above-mentioned object of the invention, the present invention provides a receiver autonomous integrity monitoring method for GNSS comprehensive positioning performance evaluation, comprising:

[0008] S1. Randomly select and construct a minimum sample set from the observation values ​​of all GNSS satellites of at least one GNSS system in the current epoch;

[0009] S2. Using the minimum sample set to solve the reference position solution vector of the receiver;

[0010] S3. Calculate the residual value of the post-positioning pseudorange of each GNSS satellite corresponding to the selected observation value according to the reference position solution vector of the receiver;

[0011] S4. Calculate the spatial precision factor of the current epoch;

[0012] S5. Using the post-positioning pseudorange residual value to obtain a consistency set under the minimum sample set;

[0013] S6. using the post-positioning pseudorange residual value and the spatial precision factor to obtain the evaluation function of the minimum sample set, and calculating the evaluation function value under the minimum sample set;

[0014] S7. randomly selecting a new minimum sample subset different from the previous minimum sample set from the current epoch, and repeating steps S1 to S6, continuously updating the evaluation function and the consistency set corresponding to the evaluation function until an optimal solution is obtained;

[0015] S8. Using the optimal consistency set obtained in step S7, a least squares algorithm is used to solve and obtain a receiver position solution vector of the receiver.

[0016] According to one aspect of the present invention, in step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, if it is a single GNSS system, then the observation values ​​of 4 satellites are randomly selected in the single GNSS system to construct the minimum sample set;

[0017] If there are multiple GNSS systems, the observation values ​​of at least 5 satellites are randomly selected from the multiple GNSS systems to construct the minimum sample set.

[0018] According to one aspect of the present invention, if there are multiple GNSS systems, then in step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, one observation value is randomly selected from each of the GNSS systems, and then the observation value is randomly selected from the observation values ​​of all other GNSS systems to construct the minimum sample set.

[0019] According to one aspect of the present invention, in step S2, the step of using the minimum sample set to solve the reference position solution vector of the receiver includes:

[0020] Construct the GNSS observation equations, which are expressed as:

[0021] GΔx0=b

[0022] Wherein, G is the satellite direction cosine matrix of the minimum sample set; Δx0 is the reference position solution vector composed of the three-dimensional coordinate position and clock error of the receiver; b is the residual vector of the pseudo-range observation value before GNSS satellite positioning;

[0023] The least squares algorithm is used to solve the GNSS observation equations to obtain the reference position solution vector, which is expressed as:

[0024] Δx0=(G T G)-1 G T b.

[0025] According to one aspect of the present invention, in step S3, the step of calculating the post-positioning pseudorange residual value of each of the GNSS satellites corresponding to the selected observation value according to the reference position solution vector of the receiver includes:

[0026] Obtain the reference position solution vector Δx0 of the receiver, and use the reference position solution vector Δx0 as a reference to calculate the cosine matrix G of all the GNSS satellite directions corresponding to the selected observation value under this reference position and the pre-positioning pseudorange residual b i (i=1,2,…,n);

[0027] The residual pseudo-range value after positioning of each GNSS satellite is calculated. Wherein n is the number of the GNSS satellites.

[0028] According to one aspect of the present invention, the residual pseudo-range value after positioning of each GNSS satellite is calculated. In the step of, if the change in the three-dimensional coordinate position of the receiver and the difference is within the preset value range, then the post-positioning pseudorange residual value It is expressed as:

[0029]

[0030] According to one aspect of the present invention, in step S5, the step of obtaining a consistency set under the minimum sample set using the post-positioning pseudorange residual value includes:

[0031] Using the post-positioning pseudorange residual value, setting a threshold;

[0032] Check one by one whether the post-positioning pseudorange residual value of each GNSS satellite is less than the threshold when constructing the minimum sample set. If so, retain the post-positioning pseudorange residual value and use it to construct the consistency set; otherwise, delete the post-positioning pseudorange residual value.

[0033] According to one aspect of the present invention, in step S6, the step of obtaining the evaluation function of the minimum sample set by using the post-positioning pseudorange residual and the spatial precision factor, and calculating the evaluation function value under the minimum sample set includes:

[0034] The evaluation function of the minimum sample set is obtained by using the post-positioning pseudorange residual value and the spatial precision factor, and the evaluation function is expressed as:

[0035]

[0036] The evaluation function is solved by weighted least square method to obtain the evaluation function value:

[0037]

[0038] Where P = W T W,W are weight matrices, PDOP is the spatial precision factor, is the pseudorange residual value after positioning, and n is the number of the GNSS satellites.

[0039] According to one aspect of the present invention, in step S8, using the optimal consistency set obtained in step S7, using the least squares algorithm to solve the receiver position solution vector of the receiver, the receiver position solution vector is expressed as:

[0040] Δx=(G cs T G cs ) -1 G cs T b cs

[0041] Among them, Δx represents the receiver position solution vector, Gcs represents the direction cosine matrix of the GNSS satellite in the optimal consistency set, and bcs represents the residual vector of the pre-positioning pseudorange observation value of the GNSS satellite in the optimal consistency set.

[0042] According to a solution of the present invention, the present invention effectively eliminates the problem that the traditional RANSAC algorithm only relies on a single variable evaluation function of the positioning pseudorange residual, and can better select a consistency set to improve the positioning performance.

[0043] According to a solution of the present invention, the solution optimizes the traditional RANSAC algorithm with the best comprehensive positioning performance as the evaluation criterion. The user equivalent range error (UERE) of the preferred constellation is approximately estimated by using the residual satellite pseudorange error after positioning. Furthermore, by introducing the satellite constellation PDOP precision factor, the optimization and transformation of the RANSAC algorithm evaluation function is achieved.

[0044] According to a solution of the present invention, an improved cost function is used to evaluate the quality of the MSS and its CS, and the minimum sample subset is randomly selected iteratively to minimize the cost function, that is, the optimal MSS and CS are found, thereby screening out those measurements with large measurement errors. When enough samples are selected and iterative calculations are performed, RANSAC can always find the MSS and CS with the best consistency, and has more advantages than RAIM in detecting multiple error measurements.

[0045] According to a solution of the present invention, the method provided by the present invention can effectively eliminate those measurements with low confidence (i.e., large measurement errors). Thus, it plays a similar role to the traditional receiver autonomous integrity algorithm, and can well eliminate the situation where multiple measurement errors are large. For the problem that the traditional RANSAC algorithm only relies on the single variable evaluation function of the positioning pseudorange residual, this method adopts a better evaluation method, can obtain a better positioning solution, and improve the positioning performance of the user receiver. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The following is a block diagram schematically showing the steps of a method for autonomous integrity monitoring of a receiver according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 are briefly introduced below.

[0048] like Figure 1 As shown, according to an embodiment of the present invention, a receiver autonomous integrity monitoring method for GNSS comprehensive positioning performance evaluation of the present invention includes:

[0049] S1. Randomly select and construct a minimum sample set from the observation values ​​of all GNSS satellites of at least one GNSS system in the current epoch;

[0050] S2. Using the minimum sample set to solve the reference position solution vector of the receiver;

[0051] S3. Calculate the residual pseudorange values ​​of each GNSS satellite after positioning corresponding to the selected observation value according to the reference position solution vector of the receiver;

[0052] S4. Calculate the spatial precision factor of the current epoch;

[0053] S5. Use the residual value of pseudo-range after positioning to obtain the consistency set under the minimum sample set;

[0054] S6. Obtain the evaluation function of the minimum sample set using the residual value of the pseudorange after positioning and the spatial precision factor, and calculate the evaluation function value under the minimum sample set;

[0055] S7. randomly select a new minimum sample subset from the current epoch that is different from the previous minimum sample set, and repeat steps S1 to S6, continuously updating the evaluation function and the consistency set corresponding to the evaluation function until the optimal solution is obtained;

[0056] S8. Using the optimal consistency set obtained in step S7, a least squares algorithm is used to solve and obtain a receiver position solution vector of the receiver.

[0057] like Figure 1 As shown, according to an embodiment of the present invention, in step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, a minimum sample subset (Minimal Sample Set, MSS) is randomly selected from the GNSS satellites and their pseudorange measurements. In this embodiment, if it is a single GNSS system, the observation values ​​of 4 satellites are randomly selected in the single GNSS system to construct the minimum sample set; if it is a plurality of GNSS systems, the observation values ​​of at least 5 satellites are cross-randomly selected in the plurality of GNSS systems to construct the minimum sample set. For example, in a dual GNSS system, the observation values ​​of 5 satellites are cross-randomly selected, and so on.

[0058] According to one embodiment of the present invention, if there are multiple GNSS systems, then in step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, one observation value is randomly selected from each GNSS system, and then an observation value is randomly selected from the observation values ​​of all other GNSS systems to construct a minimum sample set.

[0059] like Figure 1 As shown, according to an embodiment of the present invention, in step S2, in the step of using the minimum sample set to solve the reference position solution vector of the receiver, the MSS obtained in step 1 is used to solve the receiver reference position solution vector Δx0, including:

[0060] Construct the GNSS observation equations, which are expressed as:

[0061] GΔx0=b

[0062] Where G is the satellite direction cosine matrix of the minimum sample set; Δx0 is the reference position solution vector composed of the receiver's three-dimensional coordinate position and clock error; b is the residual vector of the pseudorange observation value before GNSS satellite positioning;

[0063] The least squares algorithm is used to solve the GNSS observation equations to obtain the reference position solution vector, which is expressed as:

[0064] Δx0=(G T G) -1 G T b.

[0065] like Figure 1 As shown, according to an embodiment of the present invention, in step S3, the step of calculating the post-positioning pseudorange residual value of each GNSS satellite corresponding to the selected observation value according to the reference position solution vector of the receiver includes:

[0066] Obtain the reference position solution vector Δx0 of the receiver, and use the reference position solution vector Δx0 as a benchmark to calculate the cosine matrix G of all GNSS satellite directions corresponding to the selected observation value under this reference position and the pre-positioning pseudorange residual b i (i=1,2,…,n);

[0067] Then the residual pseudo-range value of each GNSS satellite after positioning is calculated. Where n is the number of GNSS satellites.

[0068] According to an embodiment of the present invention, the residual pseudo-range value after positioning of each GNSS satellite is calculated. In the step, if the change in the three-dimensional coordinate position of the receiver and the difference is within the preset value range, the residual value of the pseudorange after positioning is It is expressed as:

[0069]

[0070] According to an embodiment of the present invention, in step S5, the step of obtaining a consistency set under a minimum sample set using the residual value of the pseudorange after positioning includes:

[0071] Using the residual value of pseudo-range after positioning, a threshold is set;

[0072] When constructing the minimum sample set, check one by one whether the residual pseudorange value after positioning of each GNSS satellite is less than the threshold value. If it is less than, the residual pseudorange value after positioning is called an "inner point", and the residual pseudorange value after positioning is retained. Otherwise, the residual pseudorange value after positioning is called an "outer point", and the residual pseudorange value after positioning is deleted. In this embodiment, the set of residual pseudorange values ​​after positioning composed of each "inner point" is called the consistency set (CS) of the MSS.

[0073] According to an embodiment of the present invention, in step S6, the step of obtaining the evaluation function of the minimum sample set by using the pseudorange residual and the spatial precision factor after positioning, and calculating the evaluation function value under the minimum sample set includes:

[0074] The evaluation function of the minimum sample set is obtained by using the residual pseudorange value and spatial precision factor after positioning. The evaluation function is expressed as:

[0075]

[0076] The weighted least squares method is used to solve the evaluation function, and the evaluation function value is obtained as follows:

[0077]

[0078] Where P = W T W,W are weight matrices, PDOP is the spatial precision factor, is the pseudorange residual value after positioning, and n is the number of GNSS satellites.

[0079] According to one embodiment of the present invention, in step S7, in the current epoch, a minimum sample subset different from the previous one is randomly selected from the observation values ​​in step S1, and the above steps one to six are repeated. The obtained evaluation function is compared with the optimal value obtained previously, and the smaller one is selected, and the process is repeated until the optimal value is selected.

[0080] like Figure 1 As shown, according to an embodiment of the present invention, in step S8, the receiver position solution vector of the receiver is obtained by using the optimal consistency set obtained in step S7 and adopting the least squares algorithm, and the receiver position solution vector is expressed as:

[0081] Δx=(G cs T G cs ) -1 G cs T b cs

[0082] Among them, Δx represents the receiver position solution vector, Gcs represents the direction cosine matrix of the GNSS satellite in the optimal consistency set, and bcs represents the residual vector of the pre-positioning pseudorange observation value of the GNSS satellite in the optimal consistency set.

[0083] To further illustrate the present invention, this scheme is further illustrated by examples.

[0084] S1. Randomly select observations of 4 satellites in a single GNSS system as a minimum sample set (MSS); in this embodiment, first select all GNSS satellites gi Among the (i=1,2,…n) observations (n ​​represents the total number of satellites), randomly select the observations gj, gk, gp, gq of 4 satellites, where 1≤j<k<p<q≤n. (Note: If it is a binary system, it is necessary to select the observations of 5 satellites. The selection method is to first randomly select 1 observation in each constellation, and then randomly select 3 from the remaining observations to ensure that in the selected minimum sample subset, there are observations in both constellation systems, and so on for multiple constellations).

[0085] S2. The MSS obtained in step S1 is used to solve the receiver reference position solution vector Δx0. In this embodiment, the GNSS satellites and their pseudo-range measurements of the minimum sample set obtained in step S1 are used to solve the receiver reference position solution vector Δx0 using the least squares algorithm. According to the basic principle of satellite navigation positioning, the GNSS observation equation group has a matrix form as shown in formula (1):

[0086] GΔx0=b (1)

[0087] Where G is the satellite direction cosine matrix of the minimum sample subset; Δx0 is the initial position solution vector composed of the three-dimensional coordinate position and clock error of the user receiver; b is the residual vector of the pseudorange observation value before GNSS satellite positioning.

[0088] Using the least squares algorithm to solve equation (1), we have:

[0089] Δx0=(G T G) -1 G T b (2).

[0090] S3. According to the receiver reference position solution vector Δx0, the residual pseudo-range value after positioning of each GNSS satellite is calculated Where n is the number of GNSS satellites; in this embodiment, according to the receiver reference position solution vector Δx0, the reference position solution vector is used as a reference to calculate the direction cosine matrix G of all satellites under this reference position and the pseudo-range residual b before positioning i (i=1,2,…,n), and then calculate the residual pseudo-range value after positioning of each GNSS satellite Wherein n is the number of the GNSS satellites.

[0091] Preferably, it is not necessary to solve the positioning equation (2) before calculating the post-positioning pseudorange residual Assuming that the change in the three-dimensional coordinate position and clock error of the user receiver is very small (i.e., the change is within the preset value), the satellite direction cosine matrix G before and after positioning can be considered equal. Therefore, the pseudo-range residual vector b before positioning can be quickly converted to the pseudo-range residual vector after positioning using equation (3):

[0092]

[0093] S4. Calculate the spatial precision factor PDOP at this epoch; in this embodiment, the direction cosine matrix G of each satellite obtained in step S3 is used to obtain the weight coefficient matrix:

[0094] H=(G T G) -1 (4)

[0095] In the above formula, H is usually called the weight coefficient matrix. When it is a single GNSS star system, it is a 4×4 symmetric matrix and can be expressed as:

[0096]

[0097] The expression for obtaining the spatial precision factor PDOP is:

[0098]

[0099] The above equations (4) and (5) are used to obtain the position precision factor (PDOP) at this epoch.

[0100] S5. Using the residual value of pseudo-range after positioning A threshold δ is set, and then the residual pseudorange value of each satellite after positioning is checked one by one to see if it is less than δ. If it is, the pseudorange measurement value is called an "inner point" and is retained. Otherwise, it is called an "outer point" and is deleted. The pseudorange measurement value set composed of the inner points is called the consistency set (ConsistencySet, CS) of the MSS. In this embodiment, the residual pseudorange b before positioning of each satellite obtained by S3 is used. i (i=1,2,…,n), and evaluate the consistency between the remaining measurements and the selected minimum sample subset MSS one by one. If the residual after positioning of a pseudorange measurement value relative to the MSS reference position solution is within a given threshold δ, then the measurement value is called an "inlier" and is retained; otherwise, it is called an "outlier" and is deleted. The set of measurements composed of each inlier is called the consistency set CS of the MSS.

[0101] S6. Use the residual pseudo-range and spatial precision factor after positioning to make an approximate assessment of the consistency between the various measurements and the error size of the position solution, and use formula (6) to calculate the residual pseudo-range after comprehensive satellite positioning at this epoch: And the evaluation function of GNSS constellation PDOP:

[0102]

[0103] When the weighted least squares solution is used, we have:

[0104]

[0105] Where P = W T W, W are weight matrices;

[0106] S7. Randomly select a minimum sample subset that is different from the previous one, repeat the above S1 to S6, compare the obtained evaluation function with the previously obtained optimal value, select the smaller one, and repeat until the optimal value is selected; in this embodiment, through the previous steps S1 to S6, a set of MSS consistency set CS and the evaluation function value under this CS are obtained, and this data is recorded, and then a set of minimum sample subsets that is different from the previous one is reselected, and steps S1 to S6 are repeated to obtain a new CS and the evaluation function value under this CS, and the evaluation function value obtained this time is compared with the previous one, and the set of data with the smaller evaluation function value is retained.

[0107] Repeat this operation until the optimal CS is selected and end this operation.

[0108] S8. Using the optimal CS obtained in S7, the least squares algorithm is used to re-solve the new receiver position solution vector Δx.

[0109] Δx=(G cs T G cs ) -1 G cs T b cs (8)

[0110] In the formula, G cs represents the direction cosine matrix of the “inside point” satellite in the selected optimal consistency set, b cs Represents the residual vector of pseudorange observations before positioning of the “inlier” satellite in the optimal consistency set.

[0111] According to the present invention, the RANSAC receiver autonomous integrity monitoring method for evaluating the GNSS comprehensive positioning performance provided by the present invention can effectively eliminate those measurements with low confidence (i.e., large measurement errors). Thus, it plays a similar role to the traditional receiver autonomous integrity algorithm, and can well eliminate the situation where multiple measurement errors are large. For the problem that the traditional RANSAC algorithm only relies on the single variable evaluation function of the positioning pseudorange residual, this method adopts a better evaluation method, can obtain a better positioning solution, and improve the positioning performance of the user receiver.

[0112] The above contents are merely examples of specific solutions of the present invention. For devices and structures not described in detail therein, it should be understood that they can be implemented by adopting general devices and general methods available in the art.

[0113] The above is only one solution of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A receiver autonomous integrity monitoring method for GNSS integrated positioning performance evaluation, comprising: S1. Randomly select and construct a minimum sample set from the observation values ​​of all GNSS satellites of at least one GNSS system in the current epoch; S2. Using the minimum sample set to solve the reference position solution vector of the receiver; S3. Calculate the residual value of the post-positioning pseudorange of each GNSS satellite corresponding to the selected observation value according to the reference position solution vector of the receiver; S4. Calculate the spatial precision factor of the current epoch; S5. Using the post-positioning pseudorange residual value to obtain a consistency set under the minimum sample set; S6. using the post-positioning pseudorange residual value and the spatial precision factor to obtain the evaluation function of the minimum sample set, and calculating the evaluation function value under the minimum sample set; S7. randomly selecting a new minimum sample subset different from the previous minimum sample set from the current epoch, and repeating steps S1 to S6, continuously updating the evaluation function and the consistency set corresponding to the evaluation function until an optimal solution is obtained; S8. Using the optimal consistency set obtained in step S7, a least squares algorithm is used to solve and obtain a receiver position solution vector of the receiver.

2. The receiver autonomous integrity monitoring method according to claim 1, characterized in that: In step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, if it is a single GNSS system, then randomly selecting observation values ​​of 4 satellites in the single GNSS system to construct the minimum sample set; If there are multiple GNSS systems, the observation values ​​of at least 5 satellites are randomly selected from the multiple GNSS systems to construct the minimum sample set.

3. The receiver autonomous integrity monitoring method according to claim 2, characterized in that: If there are multiple GNSS systems, then in step S1, in the step of randomly selecting and constructing a minimum sample set from all observation values ​​of the current epoch of at least one GNSS system, one observation value is randomly selected from each of the GNSS systems, and then the observation value is randomly selected from the observation values ​​of all other GNSS systems to construct the minimum sample set.

4. The receiver autonomous integrity monitoring method according to claim 3, characterized in that: In step S2, the step of using the minimum sample set to solve the reference position solution vector of the receiver includes: Construct the GNSS observation equations, which are expressed as: GΔx0=b Wherein, G is the satellite direction cosine matrix of the minimum sample set; Δx0 is the reference position solution vector composed of the three-dimensional coordinate position and clock error of the receiver, that is, the reference position solution vector of the receiver; b is the residual vector of the pseudorange observation value before GNSS satellite positioning; The least squares algorithm is used to solve the GNSS observation equations to obtain the reference position solution vector of the receiver, which is expressed as: Δx0=(G T G) -1 G T b。 5. The receiver autonomous integrity monitoring method according to claim 4, characterized in that: In step S3, the step of calculating the post-positioning pseudorange residual value of each of the GNSS satellites corresponding to the selected observation value according to the reference position solution vector of the receiver includes: Obtain the reference position solution vector Δx0 of the receiver, and use the reference position solution vector Δx0 of the receiver as a reference to calculate the satellite direction cosine matrix G and the pre-positioning pseudorange residual b of all the minimum sample sets corresponding to the selected observation value under this reference position i , where i = 1, 2, ..., n; The residual pseudo-range value after positioning of each GNSS satellite is calculated. Wherein, i=1, 2, ..., n, and n is the number of the GNSS satellites.

6. The receiver autonomous integrity monitoring method according to claim 5, characterized in that: Calculate the residual value of the pseudo-range after positioning of each GNSS satellite In the step of, if the change in the three-dimensional coordinate position and the clock error of the receiver is within the preset value range, then the post-positioning pseudo-range residual value It is expressed as:

7. The receiver autonomous integrity monitoring method according to claim 6, characterized in that: In step S5, the step of obtaining a consistency set under the minimum sample set by using the post-positioning pseudorange residual value includes: Using the post-positioning pseudorange residual value, setting a threshold; Check one by one whether the post-positioning pseudorange residual value of each GNSS satellite is less than the threshold when constructing the minimum sample set. If so, retain the post-positioning pseudorange residual value and use it to construct the consistency set; otherwise, delete the post-positioning pseudorange residual value.

8. The receiver autonomous integrity monitoring method according to claim 7, characterized in that: In step S6, the step of obtaining the evaluation function of the minimum sample set by using the post-positioning pseudorange residual and the spatial precision factor, and calculating the evaluation function value under the minimum sample set includes: The evaluation function of the minimum sample set is obtained by using the post-positioning pseudorange residual value and the spatial precision factor, and the evaluation function is expressed as: The evaluation function is solved by weighted least square method to obtain the evaluation function value: Where P = W T W,W are weight matrices, PDOP is the spatial precision factor, is the pseudorange residual value after positioning, and n is the number of the GNSS satellites.

9. The receiver autonomous integrity monitoring method according to claim 8, characterized in that: In step S8, the receiver position solution vector of the receiver is obtained by using the optimal consistency set obtained in step S7 and adopting the least squares algorithm. The receiver position solution vector is expressed as: Δx=(G cs T G cs ) -1 G cs T b cs Where Δx represents the receiver position solution vector, G cs represents the direction cosine matrix of the GNSS satellites in the optimal consistency set, b cs represents the residual vector of the pre-positioning pseudorange observation values ​​of the GNSS satellites in the optimal consistency set.

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