Receiver autonomous integrity monitoring method suitable for complex environment

Abstract

The invention discloses a receiver autonomous integrity monitoring method suitable for a complex environment. The method comprises the following steps: firstly, constructing a weighting model based on a vehicle course angle; then, acquiring a pseudo-range observed quantity of a satellite and preprocessing the pseudo-range observed quantity, and establishing a criterion for evaluating the comprehensive positioning precision of a current consistent measured value by utilizing pseudo-range residues after positioning, so as to form a satellite interior point subset of the epoch; and finally, carrying out approximate estimation on a user equivalent distance measurement error of a preferred constellation in the epoch satellite interior point subset, introducing a position geometric accuracy factor of the satellite constellation, constructing an evaluation function, selecting an optimal sample set, and finally solving the optimal sample set by adopting a weighted least squares (WLS) method to obtain a distance measurement result. And obtaining a final position solution result of the receiver. According to the invention, the positioning precision and fault detection capability of the receiver are improved, so that autonomous integrity monitoring of the receiver is completed.

Description

Technical Field

[0001] The invention relates to the field of satellite navigation technology, and in particular to a receiver autonomous integrity monitoring method suitable for use in complex environments. Background Art

[0002] The Global Satellite Navigation System (GNSS) is an air-based radio navigation and positioning system that can provide users with all-weather three-dimensional coordinates, speed and time information at any location on the Earth's surface or in near-Earth space. It includes one or more satellite constellations and their augmentation systems required to support specific tasks. The four major satellite navigation system suppliers announced by the International Committee on Global Navigation Satellite Systems include the United States' Global Positioning System GPS, Russia's GLONASS, the European Union's Galileo Satellite Navigation System (GALILEO) and China's BeiDou Satellite Navigation System (BDS).

[0003] With the widespread application of the Global Navigation Satellite System (GNSS), the demand for positioning is increasing, but the obstruction of objects can cause problems such as satellite signal loss of lock, non-line-of-sight error and multipath error, which seriously affect the accuracy of positioning.

[0004] A weight model is a mathematical tool used for decision making and evaluation. It helps analyze and compare the pros and cons of options by assigning weights to different factors or characteristics. The basic idea of ​​the model is that each factor has different importance to the final decision, so it needs to be assigned different weights. Usually, the weight model first determines the evaluation criteria, then weights each criterion, and finally combines the scores of each criterion to obtain an overall evaluation result. Weight models are widely used in project evaluation, investment decision-making, and product selection. By combining quantitative and qualitative information, decision makers can more clearly identify the optimal solution. In addition, weight models can also provide a structured approach to complex decision-making processes, making decisions more transparent and traceable. Some common applications include multi-criteria decision analysis (MCDA) and analytic hierarchy process (AHP), which emphasize the importance of weights in the decision-making process.

[0005] RAIM, or Receiver Autonomous Integrity Monitoring, is an important technology used in the Global Navigation Satellite System (GNSS). It is mainly used to monitor and verify the integrity of received navigation signals to ensure the accuracy and reliability of positioning data. By analyzing signals from multiple satellites, RAIM is able to detect potential faults or anomalies and issue warnings in a timely manner when problems are found. This technology is particularly important for fields such as aviation and maritime that require high-precision positioning because it can prevent serious consequences caused by signal errors. The working principle of RAIM includes receiving multiple satellite signals, comparing the results of these signals to identify possible anomalies, and then taking corresponding measures, such as excluding problematic signals or issuing warnings. It does not need to rely on an external monitoring system and can autonomously complete real-time monitoring of the integrity of the satellite navigation system, thereby improving the safety and reliability of the navigation system. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method for autonomous integrity monitoring of a receiver suitable for complex environments, which optimizes the weight of satellite signals by combining the relationship between heading angle and satellite position to improve positioning accuracy and fault detection capability, and introduces the position geometry precision factor (PDOP) of the satellite constellation to optimize the evaluation function, thereby improving positioning accuracy and completing autonomous integrity monitoring of the receiver.

[0007] The technical solution of the present invention is:

[0008] A receiver autonomous integrity monitoring method suitable for use in complex environments specifically comprises the following steps:

[0009] (1) Construct a weighted model based on the vehicle heading angle;

[0010] (2) Obtain the satellite pseudorange observations and perform preprocessing, then use the pseudorange residuals after positioning to establish a criterion for evaluating the comprehensive positioning accuracy of the current consistent measurement values, thereby forming a subset of satellite inliers for this epoch;

[0011] (3) An approximate estimate of the user equivalent ranging error of the preferred constellation in the satellite point subset of this epoch is made, and the position geometric precision factor of the satellite constellation is introduced. By considering the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to select the optimal sample set. Finally, the optimal sample set is solved using the weighted least squares method (WLS) to obtain the final position solution of the receiver.

[0012] The specific steps of constructing the weighted model based on the vehicle heading angle are:

[0013] S11. Since the errors of ionospheric delay, alignment delay and multipath of satellite ranging value are highly related to the satellite elevation angle, the atmospheric delay and multipath error of satellites with smaller elevation angles are significantly larger than those of satellites with larger elevation angles. Therefore, a more ideal positioning result is obtained by using the random model of satellite elevation angle ranging error and solving it by weighted least square method, as shown in the following formula (1):

[0014]

[0015] In formula (1), σ is the standard deviation of satellite ranging error; a0 and a1 are both empirical coefficients; E0 is the satellite reference elevation angle; E is the actual satellite elevation angle; f is the proportional factor, which is determined by testing and analyzing actual data;

[0016] S12, the receiver signal carrier-to-noise ratio directly represents the quality of the navigation satellite signal. The carrier-to-noise ratio is positively correlated with the satellite signal quality, as shown in the following formula (2):

[0017]

[0018] In formula (2), C0 is 1.1×10 4 m 2 ; C / N0 is the actual carrier-to-noise ratio received by the receiver;

[0019] S13, combine formula (1) and formula (2), as shown in the following formula (3):

[0020]

[0021] In formula (3), S is the scaling factor, which is defined by the satellite signal carrier-to-noise ratio, as shown in the following formula (4):

[0022]

[0023] In formula (4), int(x) means taking the integer part of x;

[0024] S14. Based on formula (3), increase the proportional factor k, as shown in the following formula (5):

[0025]

[0026] Set a as the acute angle between the satellite and the straight line where the vehicle heading angle is located, and 0°≤a≤90°. When the satellite is close to the heading angle direction, it is considered that the satellite is less likely to be affected by the occlusion and therefore has a relatively large weight; vice versa;

[0027] The proportional factor k is set to be expressed by a sinusoidal function model: k = sinα or an exponential function model: Taking α0 = 90°, which is the reference value of the relative angle between the satellite and the vehicle heading angle, the weighted model based on the sine function model and the exponential function model is obtained, as shown in the following formula (6):

[0028]

[0029] The specific steps of obtaining the pseudorange observations of the satellite and preprocessing them, and then establishing the criterion for evaluating the comprehensive positioning accuracy of the current consistent measurement values ​​by using the pseudorange residuals after positioning, thereby forming the satellite internal point subset of this epoch are:

[0030] S21, collect pseudo-range observations from multiple satellites, remove obviously erroneous data, and normalize the data;

[0031] S22, obtain the pseudorange observation value y of each satellite, randomly select a minimum sample set MSS from all processed pseudorange observation values ​​of the current epoch, and group all pseudorange observation values ​​into the minimum sample set MSS; when all are single-satellite constellations, the number of satellites in the minimum sample subset is four, and the selection method has a total of types, n is the total number of satellites; when there are multiple satellite constellations, the satellite selection rule of the minimum sample subset is: the total number of satellites in the minimum sample subset is N+3, that is, at least one satellite is selected in each constellation, where N represents the number of constellations;

[0032] S23, use the selected minimum sample set MSS to find the residual vector v composed of the remaining n-4 residuals, and use the selected 4-star subset Sub j , and the least squares method LS to solve the receiver position x j , see the following formula (7) for details:

[0033]

[0034] In formula (7), y j Represents the predicted pseudorange value of the satellite, G j is the satellite position matrix;

[0035] S24, using the receiver position obtained by the current minimum sample set MSS to calculate the coefficient matrix G of the remaining n-4 satellites i , and then get the pseudo-range prediction value y of the remaining n-4 satellites j→i , see the following formula (8) for details:

[0036]

[0037] The remaining n-4 satellites have a residual vector v i The calculation of is shown in the following formula (9):

[0038]

[0039] The remaining n-4 satellites have a residual vector v i The asymptotically unbiased consistent estimator Var is formed, as shown in the following formula (10):

[0040]

[0041] In formula (10), v represents the residual vector representing all remaining satellites;

[0042] S25, through the asymptotically unbiased consistent estimator Var and the set residual threshold T v By comparison, it is preliminarily determined whether the minimum sample set MSS belongs to inliers or outliers. For details, see the following formula (11):

[0043]

[0044] When there is a faulty satellite in the selected minimum sample set MSS, the receiver position x calculated based on this MSS is j There will be a large deviation. At this time, the remaining n-4 satellites corresponding to the MSS will be contaminated, resulting in an abnormally large Var value. At this time, the minimum sample set MSS is classified into the outer point set, and the satellites entering the outer point set are counted; when there is no faulty satellite in the selected minimum sample set MSS, the MSS is classified into the inner point set, and the satellites entering the inner point set are also counted; repeat the above steps S22-S25 to group the pseudorange observation values ​​of all satellites by MSS until all satellites are selected; set the threshold for distinguishing between inner and outer points of satellites to T sv , see the following formula (12) for details:

[0045]

[0046] In formula (12), represents the frequency of the i-th satellite in the outlier set; all satellites detected to have faults constitute the satellite outlier subset Sub outliers ;

[0047] Then, the total satellite set is used to remove the satellite outlier subset Sub outliers , thus forming the satellite interior point subset Sub of this epoch inliers , satellite internal point subset Sub inliers The set of pseudorange observation values ​​is called the consistency set CS of the minimum sample set MSS.

[0048] The user equivalent ranging error of the preferred constellation in the satellite point subset of this epoch is approximately estimated, and the position geometry precision factor of the satellite constellation is introduced. By considering the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to select the optimal sample set. Finally, the optimal sample set is solved by the weighted least squares method WLS to obtain the final position solution result of the receiver. The specific steps are as follows:

[0049] S31. Define the number of satellites in the minimum sample set MSS as m, and the distance of the other nm satellites relative to the MSS positioning solution as b i (i = 1, 2, ..., nm), that is, the difference between the measured pseudorange value and the calculated pseudorange value, all b i The pseudo-range residual vector b is formed, and then the sum function of the distance vector b is defined through the evaluation function form, as shown in the following formula (13):

[0050]

[0051] In formula (13), δ is a threshold parameter used to determine whether the pseudorange residual bi is within an acceptable range; b i is the i-th element in the pseudo-range residual vector b after positioning, and satisfies the following equation (14):

[0052]

[0053] After calculating the evaluation function C(b) of the current minimum sample set MSS and consistency set CS, compare it with the evaluation function with the previous minimum value. If the value of the current evaluation function C(b) is smaller, update it to the current optimal minimum sample set MSS and consistency set CS;

[0054] S32. Assume that for each selected minimum sample set MSS, the probability that the remaining pseudorange observations belong to the MSS is q; then the probability that at least one of the remaining pseudorange observations does not belong to the MSS, that is, there is at least one outlier, is 1-q; then, for the h consecutively selected MSSs, the probability that there is at least one abnormal pseudorange observation is (1-q) h , the value of h satisfies (1-q) h <ε, ε is the required false alarm rate, and the above relationship is expressed in the following formula (15):

[0055]

[0056] According to formula (15), when a false alarm rate ε is given, the stopping threshold T of the iterative calculation is randomly selected by this method;

[0057] Define that the probability of each independent pseudorange observation being selected as MSS is the same, then q is expressed as: n inlier is the number of inliers estimated in all pseudorange observations, approximately the sample size of the current optimal minimum sample set MSS and its consistency set CS;

[0058] S33. Due to the existence of gross error pseudorange observations, the consistency between the pseudorange observations will be destroyed, and a large positioning error will be brought about, so that the pseudorange residual after positioning exceeds the threshold. The positioning accuracy of GNSS depends not only on the size of the satellite pseudorange measurement error, but also on the position geometry precision factor PDOP of the satellite constellation, as shown in the following formula (16):

[0059] σ p =PDOP×σ UERE (16);

[0060] In formula (16), σ p is the three-dimensional coordinate position positioning error of the user receiver, σ UERE is the user equivalent ranging error;

[0061] S34. Define the user equivalent ranging error σ for each known satellite i (i=1, 2,...,n), then σ UERE The calculation of is shown in the following formula (17):

[0062]

[0063] Due to σ UERE It cannot be obtained directly, so the pseudorange residual after positioning is used The user equivalent ranging error is approximated as shown in the following formula (18):

[0064]

[0065] S34. When the weighted least squares method WLS is used to solve the receiver position solution, equation (18) is transformed into equation (19):

[0066]

[0067] In formula (19), C represents the weighting matrix, which is a diagonal matrix. The elements on its diagonal reflect the quality of each satellite signal and are determined based on the carrier-to-noise ratio of the satellite.

[0068] Then the three-dimensional positioning error σ of the user receiver is p For approximate evaluation, see the following formula (20):

[0069] σ p ≈PDOP×σb (20);

[0070] In formula (20), PDOP represents the position geometry precision factor of the satellite constellation;

[0071] Then the evaluation function is defined as The calculation process is shown in the following formula (21):

[0072]

[0073] When using WLS solution:

[0074] It is then re-solved using WLS to obtain the final position solution of the receiver.

[0075] Advantages of the present invention:

[0076] The weighted model based on the vehicle heading angle constructed by the present invention optimizes the weight of the satellite signal by combining the relationship between the heading angle and the satellite position to improve the positioning accuracy and fault detection capability. Secondly, considering that the pseudo-range observations of the receiver in a complex environment are more susceptible to contamination, the present invention makes an approximate estimate of the user equivalent ranging error (UERE) of the preferred constellation, and introduces the position geometry precision factor (PDOP) of the satellite constellation. By combining the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to detect the consistency between the data set and the observation value, thereby improving the positioning accuracy and completing the autonomous integrity monitoring of the receiver. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] A receiver autonomous integrity monitoring method suitable for use in complex environments specifically comprises the following steps:

[0080] (1) Construct a weighted model based on the vehicle heading angle. The specific steps are as follows:

[0081] S11. Since the errors of ionospheric delay, alignment delay and multipath of satellite ranging value are highly related to the satellite elevation angle, the atmospheric delay and multipath error of satellites with smaller elevation angles are significantly larger than those of satellites with larger elevation angles. Therefore, a more ideal positioning result is obtained by using the random model of satellite elevation angle ranging error and solving it by weighted least square method, as shown in the following formula (1):

[0082]

[0083] In formula (1), σ is the standard deviation of satellite ranging error; a0 and a1 are both empirical coefficients; E0 is the satellite reference elevation angle; E is the actual satellite elevation angle; f is the proportional factor, which is determined by testing and analyzing actual data;

[0084] S12, the receiver signal carrier-to-noise ratio directly represents the quality of the navigation satellite signal. The carrier-to-noise ratio is positively correlated with the satellite signal quality, as shown in the following formula (2):

[0085]

[0086] In formula (2), C0 is 1.1×10 4 m 2 ; C / N0 is the actual carrier-to-noise ratio received by the receiver;

[0087] S13, combine formula (1) and formula (2), as shown in the following formula (3):

[0088]

[0089] In formula (3), S is the scaling factor, which is defined by the satellite signal carrier-to-noise ratio, as shown in the following formula (4):

[0090]

[0091] In formula (4), int(x) means taking the integer part of x;

[0092] S14. Based on formula (3), increase the proportional factor k, as shown in the following formula (5):

[0093]

[0094] Set a as the acute angle between the satellite and the straight line where the vehicle heading angle is located, and 0°≤a≤90°. When the satellite is close to the heading angle direction, it is considered that the satellite is less likely to be affected by the occlusion and therefore has a relatively large weight; vice versa;

[0095] The proportional factor k is set to be expressed by a sinusoidal function model: k = sinα or an exponential function model: Taking α0 = 90°, which is the reference value of the relative angle between the satellite and the vehicle heading angle, the weighted model based on the sine function model and the exponential function model is obtained, as shown in the following formula (6):

[0096]

[0097] (2) Obtain the satellite pseudorange observations and perform preprocessing. Then, use the pseudorange residuals after positioning to establish a criterion for evaluating the comprehensive positioning accuracy of the current consistent measurement values, thereby forming a subset of satellite inliers for this epoch. The specific steps are as follows:

[0098] S21, collect pseudo-range observations from multiple satellites, remove obviously erroneous data, and normalize the data;

[0099] S22, obtain the pseudorange observation value y of each satellite, randomly select a minimum sample set MSS from all processed pseudorange observation values ​​of the current epoch, and group all pseudorange observation values ​​into the minimum sample set MSS; when all are single-satellite constellations, the number of satellites in the minimum sample subset is four, and the selection method has a total of types, n is the total number of satellites; when there are multiple satellite constellations, the satellite selection rule of the minimum sample subset is: the total number of satellites in the minimum sample subset is N+3, that is, at least one satellite is selected in each constellation, where N represents the number of constellations;

[0100] S23, use the selected minimum sample set MSS to find the residual vector v composed of the remaining n-4 residuals, and use the selected 4-star subset Sub j , and the least squares method LS to solve the receiver position x j , see the following formula (7) for details:

[0101]

[0102] In formula (7), y j Represents the predicted pseudorange value of the satellite, G j is the satellite position matrix;

[0103] S24, using the receiver position obtained by the current minimum sample set MSS to calculate the coefficient matrix G of the remaining n-4 satellites i , and then get the pseudo-range prediction value y of the remaining n-4 satellites j→i , see the following formula (8) for details:

[0104]

[0105] The remaining n-4 satellites have a residual vector v i The calculation of is shown in the following formula (9):

[0106]

[0107] The remaining n-4 satellites have a residual vector v i The asymptotically unbiased consistent estimator Var is formed, as shown in the following formula (10):

[0108]

[0109] In formula (10), v represents the residual vector representing all remaining satellites;

[0110] S25, through the asymptotically unbiased consistent estimator Var and the set residual threshold T v By comparison, it is preliminarily determined whether the minimum sample set MSS belongs to inliers or outliers. For details, see the following formula (11):

[0111]

[0112] When there is a faulty satellite in the selected minimum sample set MSS, the receiver position x calculated based on this MSS is j There will be a large deviation. At this time, the remaining n-4 satellites corresponding to the MSS will be contaminated, resulting in an abnormally large Var value. At this time, the minimum sample set MSS is classified into the outer point set, and the satellites entering the outer point set are counted; when there is no faulty satellite in the selected minimum sample set MSS, the MSS is classified into the inner point set, and the satellites entering the inner point set are also counted; repeat the above steps S22-S25 to group the pseudorange observation values ​​of all satellites by MSS until all satellites are selected; set the threshold for distinguishing between inner and outer points of satellites to T sv , see the following formula (12) for details:

[0113]

[0114] In formula (12), represents the frequency of the i-th satellite in the outlier set; all satellites detected to have faults constitute the satellite outlier subset Sub outliers ;

[0115] Then, the total satellite set is used to remove the satellite outlier subset Sub outliers , thus forming the satellite interior point subset Sub of this epoch inliers , satellite internal point subset Sub inliers The set of pseudo-range observation values ​​is called the consistency set CS of the minimum sample set MSS;

[0116] (3) An approximate estimate of the user equivalent ranging error of the preferred constellation in the satellite point subset of this epoch is made, and the position geometric precision factor of the satellite constellation is introduced. By considering the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to select the optimal sample set. Finally, the optimal sample set is solved using the weighted least squares method (WLS) to obtain the final position solution of the receiver. The specific steps are as follows:

[0117] S31. Define the number of satellites in the minimum sample set MSS as m, and the distance of the other nm satellites relative to the MSS positioning solution as b i (i = 1, 2, ..., nm), that is, the difference between the measured pseudorange value and the calculated pseudorange value, all b i The pseudo-range residual vector b is formed, and then the sum function of the distance vector b is defined through the evaluation function form, as shown in the following formula (13):

[0118]

[0119] In formula (13), δ is a threshold parameter used to determine whether the pseudorange residual bi is within an acceptable range; b i is the i-th element in the pseudo-range residual vector b after positioning, and satisfies the following equation (14):

[0120]

[0121] After calculating the evaluation function C(b) of the current minimum sample set MSS and consistency set CS, compare it with the evaluation function with the previous minimum value. If the value of the current evaluation function C(b) is smaller, update it to the current optimal minimum sample set MSS and consistency set CS;

[0122] S32. Assume that for each selected minimum sample set MSS, the probability that the remaining pseudorange observations belong to the MSS is q; then the probability that at least one of the remaining pseudorange observations does not belong to the MSS, that is, there is at least one outlier, is 1-q; then, for the h consecutively selected MSSs, the probability that there is at least one abnormal pseudorange observation is (1-q) h , the value of h satisfies (1-q) h <ε, ε is the required false alarm rate, and the above relationship is expressed in the following formula (15):

[0123]

[0124] According to formula (15), when a false alarm rate ε is given, the stopping threshold T of the iterative calculation is randomly selected by this method;

[0125] Define that the probability of each independent pseudorange observation being selected as MSS is the same, then q is expressed as: n inlier is the number of inliers estimated in all pseudorange observations, approximately the sample size of the current optimal minimum sample set MSS and its consistency set CS;

[0126] S33. Due to the existence of gross error pseudorange observations, the consistency between the pseudorange observations will be destroyed, and a large positioning error will be brought about, so that the pseudorange residual after positioning exceeds the threshold. The positioning accuracy of GNSS depends not only on the size of the satellite pseudorange measurement error, but also on the position geometry precision factor PDOP of the satellite constellation, as shown in the following formula (16):

[0127] σ p =PDOP×σ UERE (16);

[0128] In formula (16), σ p is the three-dimensional coordinate position positioning error of the user receiver, σ UERE is the user equivalent ranging error;

[0129] S34. Define the user equivalent ranging error σ for each known satellite i (i=1, 2,...,n), then σ UERE The calculation of is shown in the following formula (17):

[0130]

[0131] Due to σ UERE It cannot be obtained directly, so the pseudorange residual after positioning is used The user equivalent ranging error is approximated as shown in the following formula (18):

[0132]

[0133] S34. When the weighted least squares method WLS is used to solve the receiver position solution, equation (18) is transformed into equation (19):

[0134]

[0135] In formula (19), C represents the weighting matrix, which is a diagonal matrix. The elements on its diagonal reflect the quality of each satellite signal and are determined based on the carrier-to-noise ratio of the satellite.

[0136] Then the three-dimensional positioning error σ of the user receiver is p For approximate evaluation, see the following formula (20):

[0137] σ p ≈PDOP×σb (20);

[0138] In formula (20), PDOP represents the position geometry precision factor of the satellite constellation;

[0139] Then the evaluation function is defined as The calculation process is shown in the following formula (21):

[0140]

[0141] When using WLS solution:

[0142] It is then re-solved using WLS to obtain the final position solution of the receiver.

[0143] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A receiver autonomous integrity monitoring method suitable for complex environments, characterized by: The specific steps include: (1) Construct a weighted model based on the vehicle heading angle; (2) Obtain the satellite pseudorange observations and perform preprocessing, then use the pseudorange residuals after positioning to establish a criterion for evaluating the comprehensive positioning accuracy of the current consistent measurement values, thereby forming a subset of satellite inliers for this epoch; (3) An approximate estimate of the user equivalent ranging error of the preferred constellation in the satellite point subset of this epoch is made, and the position geometric precision factor of the satellite constellation is introduced. By considering the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to select the optimal sample set. Finally, the optimal sample set is solved using the weighted least squares method (WLS) to obtain the final position solution of the receiver.

2. The receiver autonomous integrity monitoring method applicable to complex environments according to claim 1, characterized in that: The specific steps of constructing the weighted model based on the vehicle heading angle are: S11. Since the errors of ionospheric delay, alignment delay and multipath of satellite ranging value are highly related to the satellite elevation angle, the atmospheric delay and multipath error of satellites with smaller elevation angles are significantly larger than those of satellites with larger elevation angles. Therefore, a more ideal positioning result is obtained by using the random model of satellite elevation angle ranging error and solving it by weighted least square method, as shown in the following formula (1): In formula (1), σ is the standard deviation of satellite ranging error; a0 and a1 are both empirical coefficients; E0 is the satellite reference altitude angle; E is the actual altitude angle of the satellite; f is the scaling factor, which is determined through testing and analysis of actual data; S12, the receiver signal carrier-to-noise ratio directly represents the quality of the navigation satellite signal. The carrier-to-noise ratio is positively correlated with the satellite signal quality, as shown in the following formula (2): In formula (2), C0 is 1.1×10 4 m 2 ; C / N0 is the actual carrier-to-noise ratio received by the receiver; S13, combine formula (1) and formula (2), as shown in the following formula (3): In formula (3), S is the scaling factor, which is defined by the satellite signal carrier-to-noise ratio, as shown in the following formula (4): In formula (4), int(x) means taking the integer part of x; S14. Based on formula (3), increase the proportional factor k, as shown in the following formula (5): Set a as the acute angle between the satellite and the straight line where the vehicle heading angle is located, and 0°≤a≤90°. When the satellite is close to the heading angle direction, it is considered that the satellite is less likely to be affected by the occlusion and therefore has a relatively large weight; vice versa; The proportional factor k is set to be expressed by a sinusoidal function model: k = sinα or an exponential function model: Taking α0 = 90°, which is the reference value of the relative angle between the satellite and the vehicle heading angle, the weighted model based on the sine function model and the exponential function model is obtained, as shown in the following formula (6):

3. The receiver autonomous integrity monitoring method applicable to complex environments according to claim 2, characterized in that: The specific steps of obtaining the pseudorange observations of the satellite and preprocessing them, and then establishing the criterion for evaluating the comprehensive positioning accuracy of the current consistent measurement values ​​by using the pseudorange residuals after positioning, thereby forming the satellite internal point subset of this epoch are: S21, collect pseudo-range observations from multiple satellites, remove obviously erroneous data, and normalize the data; S22, obtain the pseudorange observation value y of each satellite, randomly select a minimum sample set MSS from all processed pseudorange observation values ​​of the current epoch, and group all pseudorange observation values ​​into the minimum sample set MSS; when all are single-satellite constellations, the number of satellites in the minimum sample subset is four, and the selection method has a total of types, n is the total number of satellites; when there are multiple satellite constellations, the satellite selection rule of the minimum sample subset is: the total number of satellites in the minimum sample subset is N+3, that is, at least one satellite is selected in each constellation, where N represents the number of constellations; S23, use the selected minimum sample set MSS to find the residual vector v composed of the remaining n-4 residuals, and use the selected 4-star subset Sub j , and the least squares method LS to solve the receiver position x j , see the following formula (7) for details: In formula (7), y j Represents the predicted pseudorange value of the satellite, G j is the satellite position matrix; S24, using the receiver position obtained by the current minimum sample set MSS to calculate the coefficient matrix G of the remaining n-4 satellites i , and then get the pseudo-range prediction value y of the remaining n-4 satellites j→i , see the following formula (8) for details: The remaining n-4 satellites have a residual vector v i The calculation of is shown in the following formula (9): The remaining n-4 satellites have a residual vector v i The asymptotically unbiased consistent estimator Var is formed, as shown in the following formula (10): In formula (10), v represents the residual vector representing all remaining satellites; S25, through the asymptotically unbiased consistent estimator Var and the set residual threshold T v By comparison, it is preliminarily determined whether the minimum sample set MSS belongs to inliers or outliers. For details, see the following formula (11): When there is a faulty satellite in the selected minimum sample set MSS, the receiver position x calculated based on this MSS is j There will be a large deviation. At this time, the remaining n-4 satellites corresponding to the MSS will be contaminated, resulting in an abnormally large Var value. At this time, the minimum sample set MSS is classified into the outer point set, and the satellites entering the outer point set are counted; when there is no faulty satellite in the selected minimum sample set MSS, the MSS is classified into the inner point set, and the satellites entering the inner point set are also counted; repeat the above steps S22-S25 to group the pseudorange observation values ​​of all satellites by MSS until all satellites are selected; set the threshold for distinguishing between inner and outer points of satellites to T sv , see the following formula (12) for details: In formula (12), represents the frequency of the i-th satellite in the outlier set; all satellites detected to have faults constitute the satellite outlier subset Sub outlilers ; Then, the total satellite set is used to remove the satellite outlier subset Sub outliers , thus forming the satellite interior point subset Sub of this epoch inliers , satellite internal point subset Sub inliers The set of pseudorange observation values ​​is called the consistency set CS of the minimum sample set MSS.

4. The receiver autonomous integrity monitoring method applicable to complex environments according to claim 3, characterized in that: The user equivalent ranging error of the preferred constellation in the satellite point subset of this epoch is approximately estimated, and the position geometry precision factor of the satellite constellation is introduced. By considering the position distribution of the satellite and the signal error received by the user end, an evaluation function is constructed to select the optimal sample set. Finally, the optimal sample set is solved by the weighted least squares method WLS to obtain the final position solution result of the receiver. The specific steps are as follows: S31. Define the number of satellites in the minimum sample set MSS as m, and the distance of the other nm satellites relative to the MSS positioning solution as b i (i = 1, 2, ..., nm), that is, the difference between the measured pseudorange value and the calculated pseudorange value, all b i The pseudo-range residual vector b is formed, and then the sum function of the distance vector b is defined through the evaluation function form, as shown in the following formula (13): In formula (13), δ is a threshold parameter used to determine whether the pseudorange residual bi is within an acceptable range; b i is the i-th element in the pseudorange residual vector b after positioning, and satisfies the following equation (14): After calculating the evaluation function C(b) of the current minimum sample set MSS and consistency set CS, compare it with the evaluation function with the previous minimum value. If the value of the current evaluation function C(b) is smaller, update it to the current optimal minimum sample set MSS and consistency set CS; S32. Assume that for each selected minimum sample set MSS, the probability that the remaining pseudorange observations belong to the MSS is q; then the probability that at least one of the remaining pseudorange observations does not belong to the MSS, that is, there is at least one outlier, is 1-q; then, for the h consecutively selected MSSs, the probability that there is at least one abnormal pseudorange observation is (1-q) h , the value of h satisfies (1-q) h <ε, ε is the required false alarm rate, and the above relationship is expressed in the following formula (15): According to formula (15), when a false alarm rate ε is given, the stopping threshold T of the iterative calculation is randomly selected by this method; Define that the probability of each independent pseudorange observation being selected as MSS is the same, then q is expressed as: n inlier is the number of inliers estimated in all pseudorange observations, approximately the sample size of the current optimal minimum sample set MSS and its consistency set CS; S33. Due to the existence of gross error pseudorange observations, the consistency between the pseudorange observations will be destroyed, and a large positioning error will be brought about, so that the pseudorange residual after positioning exceeds the threshold. The positioning accuracy of GNSS depends not only on the size of the satellite pseudorange measurement error, but also on the position geometry precision factor PDOP of the satellite constellation, as shown in the following formula (16): s p =PDOP×σ UERE (16); In formula (16), σ p is the three-dimensional coordinate position positioning error of the user receiver, σ UERE is the user equivalent ranging error; S34. Define the user equivalent ranging error σ for each known satellite i (i=1, 2,...,n), then σ UERE The calculation of is shown in the following formula (17): Due to σ UERE It cannot be obtained directly, so the pseudorange residual after positioning is used The user equivalent ranging error is approximated as shown in the following formula (18): S34. When the weighted least squares method WLS is used to solve the receiver position solution, equation (18) is transformed into equation (19): In formula (19), C represents the weighting matrix, which is a diagonal matrix. The elements on its diagonal reflect the quality of each satellite signal and are determined based on the carrier-to-noise ratio of the satellite. Then the three-dimensional positioning error σ of the user receiver is p For approximate evaluation, see the following formula (20): s p ≈PDOP×σ b (20); In formula (20), PDOP represents the position geometry precision factor of the satellite constellation; Then the evaluation function is defined as The calculation process is shown in the following formula (21): When using WLS solution: It is then re-solved using WLS to obtain the final position solution of the receiver.

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