Satellite navigation application terminal multi-fault excitation integrity evaluation method

Abstract

The invention relates to a satellite navigation application terminal multi-fault excitation integrity evaluation method. The method comprises the following steps: determining a risk source fault mode needing excitation according to a risk source fault mode occurrence probability; converting the integrity risk index into an equivalent omission ratio index, and determining the occurrence probability of each fault mode in the test sample; determining a worst fault amplitude based on a worst fault criterion; constructing an I / II type error probability evaluation model according to the equivalent omission ratio index to quantitatively evaluate the confidence coefficient; determining the number of test samples needing to be collected and an evaluation test threshold according to an I / II type error probability evaluation model, randomly selecting a plurality of test sites in a global range, determining the number of test samples of each test site, and injecting a worst fault into the test samples used in each test site; and collecting a test sample generated by the satellite navigation application terminal and evaluating whether the integrity risk of the satellite navigation application terminal meets the expectation or not.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a multiple fault stimulus integrity evaluation method for a satellite navigation application terminal. Background Art

[0002] The Global Navigation Satellite System (GNSS) has powerful functions such as navigation, positioning, and timing, and can provide all-round, all-weather, and all-time navigation and positioning services. GNSS is constantly assisting in navigation and positioning scenarios such as automatic guidance of carrier-based aircraft, automatic driving, and collaborative operation of intelligent agricultural machinery. These typical application scenarios show that GNSS has been widely used in navigation fields related to life safety. Therefore, its positioning performance services are gradually shifting from high precision to high reliability. This trend shows that integrity is becoming increasingly important in navigation fields related to life safety.

[0003] At present, the precision approach phase of the aircraft landing process uses carrier phase real-time differential positioning (Real Time Kinematic, RTK) to obtain relative positioning. In order to ensure highly reliable precision landing, it is necessary to effectively utilize the operating statistical characteristics of satellite navigation application terminals and evaluate user integrity risk indicators to provide a reference for further optimizing integrity performance.

[0004] Existing assessment methods have the following defects: satellite navigation application terminals need to face challenges from multiple risk sources such as the satellite segment, space atmosphere segment and satellite navigation application receiver terminal. The probability of occurrence of risk sources is extremely low, and the number of multi-satellite fault amplitudes is large and difficult to traverse, resulting in great testing difficulty; the test time of the precision approach phase is relatively short, and the high number of test samples required by traditional methods leads to high assessment costs. Reducing the number of test samples can easily lead to inaccurate integrity risk assessment. Summary of the invention

[0005] Aiming at the problems in scenarios such as extremely low probability of occurrence of multiple risk sources and multiple satellites, difficulty in traversing excitation of fault amplitudes of multiple risk sources and inaccurate confidence of short-term integrity assessment, the present invention proposes a multiple fault excitation integrity assessment method for satellite navigation application terminals, including:

[0006] Step S1) determining the failure mode to be stimulated according to the probability of occurrence of the risk source failure mode;

[0007] Step S2) converting the integrity risk index into an equivalent missed detection rate index using the sum of prior failure probabilities of the failure modes that need to be stimulated, and determining the occurrence probability of each failure mode in the test sample;

[0008] Step S3) determining the worst fault amplitude based on the worst fault criterion;

[0009] Step S4) constructing a type I / II error probability assessment model to quantify the assessment confidence based on the equivalent missed detection rate index converted from the integrity risk index;

[0010] Step S5) determining the number of test samples to be collected and the evaluation test threshold according to the type I / II error probability evaluation model;

[0011] Step S6) randomly selecting multiple test sites worldwide, determining the number of test samples for each test site, and injecting the worst fault into the test samples used in each test site;

[0012] Step S7) collects test samples generated by the satellite navigation application terminal and evaluates whether the integrity risk of the satellite navigation application terminal meets expectations.

[0013] Beneficial effects of the present invention:

[0014] The invention provides a method for evaluating the integrity of a satellite navigation application terminal with multiple fault excitations. The method adopts the method of injecting the worst fault and using the worst fault amplitude to solve the problem of great testing difficulty caused by the extremely low probability of risk source failure and the difficulty of multi-satellite failure excitation under the constraints of a scenario in which the probability of risk source failure is extremely low, multi-satellite failure excitation is difficult, and the confidence in the evaluation of short-time limited test samples is inaccurate. The method solves the problem by constructing a type I / II error probability evaluation model to determine the number of samples required for the test, solves the problem of inaccurate evaluation confidence caused by short-time limited test samples, and can complete high-confidence and high-efficiency testing and evaluation of the integrity risk of the satellite navigation application terminal, providing reliable guarantee for satellite-guided landing of the satellite navigation application terminal in the precision approach phase, and is of great significance in the application of navigation and positioning in fields related to life safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulations provided by the present invention;

[0016] Figure 2 A schematic diagram of the processing flow of the integrity evaluation method provided by the present invention after it is applied to a simulator and a test evaluation platform. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0018] The satellite navigation application terminal multiple fault excitation integrity assessment method proposed in the present invention can effectively cope with the scenario constraints of multiple risk sources concurrency and difficult fault amplitude excitation, and efficiently and confidently evaluate the integrity risk of the satellite navigation application terminal. The technical solution of the present invention is further specifically described below in conjunction with the accompanying drawings.

[0019] like Figure 1 As shown, the present invention provides a method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulations, comprising the following steps:

[0020] Step S1) determining the failure mode to be stimulated according to the probability of occurrence of the failure mode of the risk source.

[0021] Using typical risk sources such as satellite ephemeris failure, atmospheric anomaly failure, multipath anomaly, and cycle slips that are known to affect satellite navigation application terminals, the failure mode that needs to be stimulated is determined by comparing the published or statistically available prior failures with the integrity risk indicators of the satellite navigation application terminal that needs to be tested. This failure mode refers to the failure state of the satellite observation quantity caused by the abnormality of the risk source, so it can also be called the risk source failure mode. The failure mode is determined to be a random failure on a single or multiple satellites after the combination of the above-mentioned multiple risk sources. The probability of the risk source failure of the failure mode under a single-satellite failure is the prior failure probability of each risk source, and the probability of the failure under a multi-satellite failure condition is the product of the prior failure probabilities after traversing the two types of elements such as the combined satellite and the risk source; the risk source includes satellite ephemeris failure, atmospheric anomaly failure, multipath anomaly, cycle slip, etc. The risk source (or failure) is simply expressed as f h ,

[0022]

[0023] The probability of failure mode occurrence can be expressed as:

[0024]

[0025] In formula (2) represents the probability of occurrence of the failure mode, subscript H k (k=1,2,3,4) represents the failure mode, k=1 represents the satellite ephemeris failure mode, k=2 represents the atmospheric anomaly failure mode, k=3 represents the multipath anomaly mode, k=4 represents the cycle slip mode, H0 represents the no-fault mode, since the integrity risk caused by no-fault is extremely low compared to the probability of occurrence of the failure mode, it is not included in the test scope. f Indicates the number of failed satellites; represents the probability of occurrence of a single star failure mode, Indicates that on satellite x i (i=1,2,…,n s ) Failure occurred on hIt should be noted that the probability of each risk source (failure) occurring on each satellite is assumed to be the same. Indicates; n s Indicates the number of satellites involved in positioning; It represents the probability of occurrence of multi-star failure mode. N sat,max Indicates the maximum number of faulty satellites, which is determined in the subsequent process of determining the risk source failure mode required for excitation.

[0026] Next, determine the risk source failure mode required for integrity assessment. The specific determination method is:

[0027] (1) Obtain the cumulative probability of occurrence of single-star failure mode to the cumulative probability of occurrence of multiple-star failure mode,

[0028]

[0029] In formula (3), N sat,max represents the maximum number of failed satellites, N total is the total number of failure modes that need to be stimulated, Represents the number of combinations, n s Indicates the number of satellites involved in positioning, m f represents the number of failed satellites, P fh Indicates that each satellite has a failure f h The probability of represents the probability of failure mode occurrence, H k (k=1,2,3,4) represents the failure mode, represents the probability of occurrence of multi-star failure mode, Indicates the number of combinations.

[0030] (2) Based on the fact that the sum of the calculated cumulative failure mode occurrence probabilities is greater than the probability of good risk failure events that need to be considered, in order to simplify the calculation, conservative upper and lower bounds are used to determine the maximum number of faulty satellites and the total number of failure modes that need to be stimulated. Specifically,

[0031]

[0032] In formula (4), N sat,max represents the maximum number of failed satellites, N total is the total number of fault modes that need to be stimulated, P IR It represents the integrity risk index of satellite navigation application terminal. Represents the probability of failure mode occurrence.

[0033] Step S2) The integrity risk index is converted into an equivalent missed detection rate index using the sum of priori failure probabilities of the failure modes that need to be stimulated, and the occurrence probability of each failure mode in the test sample is determined at the same time.

[0034] According to the classic integrity risk assessment model under the existing known failure mode, the integrity risk index is converted into an equivalent missed detection rate index, and the test index is amplified, which can greatly reduce the number of samples required. Considering that integrity risk failure events are mainly caused by failure modes, the integrity risk model under the classic failure mode is converted into an integrity risk test model, which is specifically expressed as:

[0035]

[0036] In formula (5), P IR represents the integrity risk index of satellite navigation application terminal, N total The total number of fault modes that need to be stimulated, including single-star fault modes and multi-star fault modes, represents the probability of failure mode occurrence, H k (k=1,2,3,4) represents the failure mode; HMI represents the dangerous misleading information event, and its expression is:

[0037] HMI={PE|>AL,|t|<T} (6)

[0038] In formula (6), PE is the positioning error of the satellite navigation application terminal, AL is the warning limit of the precision approach phase, t is the test statistic of the satellite navigation application terminal integrity monitoring algorithm, and T is the corresponding test threshold.

[0039] The integrity risk index is converted into an equivalent missed detection rate index by using the sum of the prior failure probabilities of the failure modes that need to be stimulated. The conversion method is as follows:

[0040]

[0041] In formula (7) It represents the equivalent missed detection rate index converted from the integrity risk index using the sum of the failure mode occurrence probabilities.

[0042] By converting formula (7), the index can be improved by at least 3 orders of magnitude, which greatly reduces the number of samples required to be collected and greatly reduces the difficulty of index testing.

[0043] In order to ensure the probability equivalence of the integrity risk index, a failure must occur for each test of the precision approach. Taking each precision approach as an integrity test sample, based on the completeness of the probability of the failure mode and the mutual exclusivity of each failure mode, the Bayesian formula can be used to determine the probability of a single failure caused by each failure mode in the integrity test sample, that is, the probability of each failure mode in the integrity test sample is determined as:

[0044]

[0045] In formula (8) Each failure mode H in the test sample is represented by k The probability of occurrence of failure mode H in a precision approach of integrity test k probability.

[0046] Step S3) determining the worst fault amplitude based on the worst fault criterion.

[0047] Due to the real number characteristics of the fault amplitude of the risk source and the fact that most faults can cause integrity failure events, the number of fault amplitudes is huge, making it difficult to traverse them. Therefore, when multiple risk sources occur simultaneously, a conservative assessment of integrity risk needs to be performed under the worst fault condition. The worst fault refers to an observation fault that causes the positioning error to exceed the positioning alarm limit and the probability of the fault detector failing to detect the maximum. The worst fault includes the single-satellite worst fault and the multi-satellite worst fault. Using the worst fault can effectively avoid traversal of fault amplitudes of various risk sources, thereby accelerating the testing process of integrity risk.

[0048] Taking the LS estimator using the least squares (LS) positioning estimation and the RB fault detector based on the least squares residual (RB) as examples, the worst fault amplitudes under the conditions of single-satellite fault and multi-satellite fault are derived.

[0049] First, the measurement model after decorrelation is constructed as:

[0050] y=Gx+v+f (9)

[0051] Where y is the observed value, and in the RTK floating point solution, y is 2n s ×1 vector, y is n under RTK fixed solution s ×1 vector, G is the double difference measurement matrix after removing the correlation of the measurement noise covariance matrix, and G is 2n in the RTK floating point solution s ×m matrix, G is n in the RTK fixed solution s ×3 matrix; v is the measurement noise, is the observed error variance, I n n s ×n s Unit matrix; f is the fault magnitude vector; n s is the number of satellites involved in positioning, and m is the least squares estimation state.

[0052] Secondly, construct the failure mode H k The fault vector f k for:

[0053]

[0054] Among them A k Indicates failure mode H k Next s ×r-dimensional faulty satellite matrix, faulty satellite matrix A k Contains only the element 0 indicating a healthy satellite and the element 1 indicating a satellite failure, n s is the number of satellites involved in positioning, r is the number of faulty satellites; b k is the r×1-dimensional observation deviation vector; e k represents the r×1-dimensional fault unit vector, which represents the ratio between the observation deviations of the faulty satellite; b k Represents the magnitude of the fault vector.

[0055] The maximum integrity risk caused by the worst fault vector can be expressed as:

[0056]

[0057] In formula (11), IR is the integrity risk index to be evaluated, PE k Failure mode H k The satellite navigation application terminal positioning error under the condition of AL is the warning limit value in the precision approach phase, t k Failure mode H k The test statistic of the satellite navigation application terminal integrity algorithm under the condition of k represents the failure mode, f k Failure mode H k The right side of equation (11) represents the maximum probability that the positioning error exceeds the positioning alarm limit and the fault detector fails to detect, that is, the maximum probability of a dangerous misleading information event (integrity failure event).

[0058] From the worst fault expression, it can be seen that the maximum fault can be obtained by obtaining the worst fault unit vector and the worst fault amplitude.

[0059] The worst fault unit vector is the unit vector that maximizes the ratio of the location error to the test statistic, which can be expressed as:

[0060]

[0061] In formula (12), represents the worst fault unit vector, which is the unit fault vector that can bring the largest positioning error under the same test statistic value, and is therefore very likely to cause missed detection by the monitoring algorithm. k represents the r×1-dimensional fault unit vector, PE k Failure mode H kThe terminal positioning error of satellite navigation application under t k Failure mode H k The test statistic of the satellite navigation application terminal integrity algorithm under k Indicates failure mode H k Next s ×r-dimensional faulty satellite matrix, s T is the transposed matrix of s, is the projection matrix from the measurement domain to the positioning domain, n s ×4 direction matrix, where q columns are 1 and the rest are 0; p = I n -G(G T G) -1 G T is the projection matrix from the measurement domain to the detection domain, In is the ns×ns unit matrix, and G is the double difference measurement matrix after removing the correlation of the measurement noise covariance matrix.

[0062] From the Rayleigh quotient solution theory, we know that when the matrix When the maximum eigenvalue is obtained, the worst fault unit vector can be obtained Specifically expressed as:

[0063]

[0064] where v max,k Indicated in the eigenvector λ i,k (i=1,2,…,r) Take the eigenvector corresponding to the largest eigenvalue.

[0065] Next, the worst fault amplitude is calculated. Under the condition of obtaining the worst fault unit vector, the worst fault amplitude can be expressed as:

[0066]

[0067] In formula (14), represents the worst fault amplitude, Q(·) represents the Gaussian tail distribution function, AL is the warning limit value in the precision approach phase, s T is the projection matrix from the measurement domain to the positioning domain, A k Indicates failure mode H k Next s ×r-dimensional faulty satellite matrix, represents the worst fault unit vector, b k represents the magnitude of the fault vector, T represents the detection threshold, σ ε represents the observed error variance, Denotes the degree of freedom as n s -m, non-centralization parameter is The chi-square distribution of .

[0068] The worst fault amplitude can be obtained by setting the upper and lower boundaries. Generally, it can be conservatively set to about 0 to 10m, that is, the lower boundary is 0 and the upper boundary is 10m.

[0069] Step S4) Based on the equivalent missed detection rate index converted from the integrity risk index, a type I / II error probability assessment model is constructed to quantify the assessment confidence.

[0070] In order to avoid inaccurate sample distribution characteristics caused by using a small number of sample tests, which leads to inaccurate assessment confidence, the present invention adopts a Type I / II error probability assessment model to quantify the assessment confidence, ensure that the assessment confidence is not inaccurate, and evaluates the integrity risk of the satellite navigation application terminal by comparing the missed samples with the assessment inspection threshold.

[0071] Missed samples refer to test samples in which the positioning error PE of the satellite navigation application terminal exceeds the warning limit AL of the precision approach phase, and the test statistic t of the integrity monitoring algorithm is lower than the corresponding test threshold T, so the satellite navigation application terminal does not alarm. By judging whether the test sample is a missed sample, the number of missed samples in all test samples can be counted. Since the LS estimator and RB fault detector are adopted, it can be determined that the test statistic of the satellite navigation application terminal is independent of the positioning error, so each test sample is independent of each other. During the evaluation, if the number of missed samples is less than or equal to the preset number of allowable missed samples, the test passes, which indicates that the integrity risk index of the satellite navigation application terminal is qualified. Otherwise, the test fails, which indicates that the integrity risk index of the satellite navigation application terminal is unqualified. The specific expression is:

[0072]

[0073] In formula (15), TS means the test is passed, TF means the test is not passed, and n MD represents the number of missed samples, and N represents the evaluation test threshold, that is, the preset number of allowable missed samples.

[0074] Therefore, the evaluation confidence can be expressed by the type I / II error probability: the type I error probability refers to the probability that the actual missed detection rate of the satellite navigation application terminal does not exceed the preset missed detection rate but fails the test, indicating the risk of false alarm; the type II error probability refers to the probability that the actual missed detection rate of the satellite navigation application terminal exceeds the preset missed detection rate but passes the test, indicating the risk of missed detection. Since the test samples are independent of each other, it can be determined that they obey the binomial distribution. Therefore, the type I / II error probability evaluation model is expressed as:

[0075]

[0076] In formula (16), P typeIis the type I error probability, P typeII is the Type II error probability, α is the Type I error probability requirement that needs to be met, and β is the Type II error probability requirement that needs to be met. α and β are generally less than 5%; is the equivalent missed detection rate index converted from the integrity risk index, represents the actual missed detection rate of the satellite navigation application terminal to be tested. The actual missed detection rate is unknown in advance, and whether it meets the equivalent missed detection rate index is determined by formula (15); TS means the test is passed, which means the equivalent missed detection rate index is met, and TF means the test is not passed, which means the equivalent missed detection rate index is not met; N represents the evaluation test threshold, NIS represents the number of independent test samples, Indicates the number of combinations.

[0077] When the estimated P typeI or P typeII The lower it is, the higher the confidence in the assessment.

[0078] The reliability of integrity assessment can be guaranteed by controlling the probability of type I error to be no higher than α and the probability of type II error to be no higher than β.

[0079] Step S5) Determine the number of test samples required to be collected and the evaluation test threshold based on the Type I / II error probability evaluation model.

[0080] In order to easily calculate the probability of type II error, the corresponding missed detection rate is generally set to K times the missed detection rate index. Therefore, the number of test samples and the evaluation test threshold must meet the following conditions:

[0081]

[0082] In formula (17), L(NIS, N, 2α) represents the lower confidence limit of the Clopper-Pearson exact confidence interval, U(NIS, N, 2β) represents the upper confidence limit of the Clopper-Pearson exact confidence interval, NIS represents the number of independent test samples, and n MD represents the number of missed samples, N represents the evaluation test threshold, the lower confidence limit is a function of NIS, N and α, and the upper confidence limit is a function of NIS, N and β. It is the equivalent missed detection rate index converted from the integrity risk index.

[0083] The number of test samples required to be collected and the evaluation test threshold are determined by setting the upper and lower bounds of the traversal. The lower and upper bounds of the number of test samples required to be collected are generally the equivalent missed detection rate index The reciprocal value and 1 / 3 of the reciprocal value. For example, when the equivalent missed detection rate index is 1e-4, the upper limit of the number of test samples is 1e4, and the lower limit of the number of test samples is 333. The evaluation test threshold is generally 1 and does not exceed the number of test samples. The evaluation test threshold generally starts from 1 and traverses to the number of test samples required to be collected. The specific operations are as follows: (1) Input the lower limit value of the number of test samples as the starting value of the test sample number traversal; (2) Traverse the evaluation test threshold from 1 to the test sample number traversal value; (3) During the evaluation test threshold traversal process, if the test sample number and the evaluation test threshold meet the conditional formula (17), then the test sample number and the evaluation test threshold at this time are selected and the traversal is stopped; if the conditional formula (17) is not met, the test sample number as the traversal value is increased by the unit step size and jumps to (2), and the unit step size is usually set to 5; (4) If the test sample number increases to the upper limit value and the evaluation test threshold traverses from 1 to the upper limit value of the test sample number, and there is still no test sample number and the evaluation test threshold that meet the conditional formula (17), it indicates that the requirements are not met, and it is necessary to relax α or β and jump to (1) to start again.

[0084] Step S6) A plurality of test sites are randomly selected worldwide, the number of test samples for each test site is determined, and the worst fault is injected into the test samples used in each test site.

[0085] Divide the global (Earth) area into K°×K° grids and randomly select N station The grid intersections are test sites, and the number of test samples at each test site is determined as:

[0086] n single =ceil(n MD / N station ) (18)

[0087] In formula (18), n single represents the number of test samples required for each test site, ceil(·) represents the upward rounding function, n MD Indicates the number of missed samples, N station Indicates the number of test sites.

[0088] A single precision approach (e.g., a single approach of 150s for civil aviation applications) is used as a test sample, and the corresponding worst fault is injected into each test sample according to the probability of occurrence of each fault mode determined in step S2. The method provided in step S2 determines the probability of occurrence of the fault mode corresponding to the worst fault, for example, selecting the fault mode H corresponding to the worst fault k The probability of The fault injection form is a step form, the amplitude is the worst fault amplitude, and the duration is selectable within the approach time period. The worst fault is injected into the test sample in the other test locations in the same way as above.

[0089] By injecting the worst fault into the test sample, including using the worst fault amplitude, the problems caused by the extremely low probability of risk source failure and the difficulty of exciting multiple star faults are solved, which greatly reduces the difficulty of testing.

[0090] Step S7) collects test samples generated by the satellite navigation application terminal and evaluates whether the integrity risk of the satellite navigation application terminal meets expectations.

[0091] The short-term positioning error and warning flag data of the satellite navigation application terminal in a single precision approach are used as a test sample. The required number of test samples generated in the satellite navigation application terminal according to step S4 is collected, and according to the equivalent missed detection rate index after integrity risk conversion, it is determined whether the collected test sample is a missed detection sample according to the definition of missed detection samples in step S4, and the number of missed detection samples in all test samples is counted, and the number of missed detection samples is compared with the evaluation inspection threshold determined in step S4 to determine whether the integrity risk of the satellite navigation application terminal meets expectations: if the number of missed detection samples is less than or equal to the preset number of allowable missed detection samples, the inspection is passed, which indicates that the integrity risk index of the satellite navigation application terminal meets expectations; otherwise, the inspection fails, which indicates that the integrity risk index of the satellite navigation application terminal does not meet expectations.

[0092] Attached Figure 2 The processing flow of applying the integrity evaluation method provided by the present invention to a multi-system satellite navigation simulator and an integrity test and evaluation device is schematically shown.

[0093] The above content is a further detailed description of the present invention in combination with specific implementation methods. It cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, some simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the scope of protection determined by the claims submitted for the present invention.

Claims

1. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulations, comprising: Step S1) determining the failure mode to be stimulated according to the probability of occurrence of the risk source failure mode; Step S2) converting the integrity risk index into an equivalent missed detection rate index using the sum of prior failure probabilities of the failure modes that need to be stimulated, and determining the occurrence probability of each failure mode in the test sample; Step S3) determining the worst fault amplitude based on the worst fault criterion; Step S4) constructing a type I / II error probability assessment model to quantify the assessment confidence based on the equivalent missed detection rate index converted from the integrity risk index; Step S5) determining the number of test samples to be collected and the evaluation test threshold according to the type I / II error probability evaluation model; Step S6) randomly selecting multiple test sites worldwide, determining the number of test samples for each test site, and injecting the worst fault into the test samples used in each test site; Step S7) collects test samples generated by the satellite navigation application terminal and evaluates whether the integrity risk of the satellite navigation application terminal meets expectations.

2. A satellite navigation application terminal multiple fault stimulus integrity evaluation method according to claim 1, wherein in step S1, the failure mode includes: Satellite ephemeris failure, atmospheric anomaly failure, multipath anomaly and cycle slip, where f h Indicates various failure modes; The method for determining the probability of occurrence of risk source failure mode is as follows: under single-satellite failure, the probability of risk source failure is the prior failure probability of each risk source; under multi-satellite failure conditions, the probability of risk source failure is the product of the prior failure probabilities after traversing the two types of elements of the combined satellite and risk source, which can be specifically expressed as: in represents the probability of occurrence of the failure mode, subscript H k Indicates the fault mode. When k=1, it indicates the satellite ephemeris fault mode. When k=2, it indicates the atmospheric anomaly fault mode. When k=3, it indicates the multipath anomaly mode. When k=4, it indicates the cycle slip mode. represents the probability of occurrence of a single star failure mode, m f Indicates the number of failed satellites, Indicates that on satellite x i The probability of failure on i = 1, 2, ..., n s , because the probability of each fault occurring on each satellite is the same, Simplified to P fh , represents the probability of occurrence of multi-star failure mode, n s Indicates the number of satellites involved in positioning, N sat,max Indicates the maximum number of failed satellites.

3. According to the method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimuli according to claim 2, wherein in step S1, the method for determining the risk source failure mode required for stimuli is: (1) Obtain the cumulative probability of occurrence of single-star failure mode to the cumulative probability of occurrence of multiple-star failure mode, Where N sat,max represents the maximum number of failed satellites, N total is the total number of failure modes that need to be stimulated, Represents the number of combinations, n s Indicates the number of satellites involved in positioning, m f Indicates the number of failed satellites, Indicates that each satellite has a failure f h The probability of represents the probability of failure mode occurrence, H k represents the failure mode, where k = 1, 2, 3, 4, It represents the probability of occurrence of multi-star failure mode; (2) Based on the fact that the sum of the calculated cumulative failure mode occurrence probabilities is greater than the probability of good risk failure events that need to be considered, the maximum number of faulty satellites and the total number of failure modes that need to be stimulated are determined: Where N sat,max represents the maximum number of failed satellites, N total is the total number of fault modes that need to be stimulated, P IR It represents the integrity risk index of satellite navigation application terminal. Represents the probability of failure mode occurrence.

4. According to the method for multiple fault stimulus integrity assessment of a satellite navigation application terminal of claim 1, wherein in step S2, the method for converting the integrity risk index into an equivalent missed detection rate index comprises: in It represents the equivalent missed detection rate index converted from the integrity risk index by using the sum of the probability of occurrence of the failure mode, N total is the total number of fault modes that need to be stimulated, H k Indicates the failure mode, P IR It represents the integrity risk index of satellite navigation application terminal. represents the probability of occurrence of the failure mode, and HMI represents a dangerous misleading information event, and its expression is: HMI={PE|>AL,|t|<T} Where PE is the positioning error of the satellite navigation application terminal, AL is the warning limit of the precision approach phase, t is the test statistic of the satellite navigation application terminal integrity monitoring algorithm, and T is the corresponding test threshold.

5. According to the method for multiple fault stimulus integrity assessment of a satellite navigation application terminal as described in claim 1, wherein in step S2, the method for determining the probability of occurrence of each fault mode in the test sample is: based on the completeness of the probability of occurrence of the fault mode and the mutual exclusivity of each fault mode, the Bayesian formula is used to determine the probability of occurrence of a single fault caused by each fault mode in a single precision approach as follows: in Each failure mode H in the test sample is represented by k The probability of occurrence, N total The total number of failure modes that need to be stimulated, represents the probability of failure mode occurrence, H k Indicates the failure mode.

6. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault excitations according to claim 1, wherein in step S3, the worst fault refers to an observed fault that causes the positioning error to exceed the positioning alarm limit and the probability of the fault detector detecting failure to reach the maximum, including a single-satellite worst fault and a multi-satellite worst fault, and the step of determining the worst fault amplitude comprises: First, the worst fault unit vector is obtained, and then the worst fault amplitude is obtained using the worst fault unit vector.

7. A satellite navigation application terminal multiple fault stimulus integrity evaluation method according to claim 6, wherein: The method for obtaining the worst fault unit vector is to obtain the unit vector with the largest ratio of the positioning error to the test statistic, which is specifically expressed as: in represents the worst fault unit vector, e k represents the r×1-dimensional fault unit vector, PE k Failure mode H k The terminal positioning error of satellite navigation application under t k Failure mode H k The test statistic of the satellite navigation application terminal integrity algorithm under k Indicates failure mode H k Next s ×r-dimensional faulty satellite matrix, is the projection matrix from the measurement domain to the positioning domain, n s ×4 direction matrix, where q columns are 1 and the rest are 0; p = I n -G(G T G) -1 G T is the projection matrix from the measurement domain to the detection domain, I n n s ×n s The unit matrix, G is the double difference measurement matrix after removing the correlation of the measurement noise covariance matrix; According to the maximum Rayleigh quotient solution theory, when the matrix (A i T pA i ) 1 / 2 ss T (A i T pA i ) -1 / 2 When the maximum eigenvalue is obtained, the worst fault unit vector can be obtained, which is expressed as: where v max,k Indicated in the eigenvector λ i,k In the matrix, i = 1, 2, ..., r, Take the eigenvector corresponding to the largest eigenvalue.

8. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulations according to claim 7, wherein: The method of using the worst fault unit vector to obtain the worst fault amplitude is: in represents the worst fault amplitude, Q(·) represents the Gaussian tail distribution function, AL is the warning limit value in the precision approach phase, s T is the projection matrix from the measurement domain to the positioning domain, A k Indicates failure mode H k Next s ×r-dimensional faulty satellite matrix, represents the worst fault unit vector, b k represents the magnitude of the fault vector, T represents the detection threshold, σ ε represents the observed error variance, Denotes the degree of freedom as n s -m, non-centrality parameter is Chi-square distribution of ; Then, the worst fault amplitude is searched by setting the upper and lower boundaries.

9. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulations according to claim 1, wherein in step S4, a method for constructing a type I / II error probability evaluation model is as follows: Where P typeI is the type I error probability, P typeII is the Type II error probability, α is the Type I error probability requirement that needs to be met, and β is the Type II error probability requirement that needs to be met; is the equivalent missed detection rate index converted from the integrity risk index, represents the actual missed detection rate of the satellite navigation application terminal to be tested; N represents the evaluation test threshold, NIS represents the number of independent test samples, TS represents the test passed, which indicates that the number of missed detection samples is less than or equal to the preset number of allowable missed detection samples, and TF represents the test failed, which indicates that the number of missed detection samples is greater than the preset number of allowable missed detection samples; The Type I error probability refers to the probability that the actual missed detection rate of the satellite navigation application terminal does not exceed the preset missed detection rate and the inspection fails, which represents the assessment of the false alarm risk; the Type II error probability refers to the probability that the actual missed detection rate of the satellite navigation application terminal exceeds the preset missed detection rate and the inspection passes, which represents the assessment of the missed detection risk; The lower the Type I error probability or Type II error probability, the higher the confidence in the assessment.

10. According to the method for multiple fault stimulus integrity assessment of a satellite navigation application terminal of claim 1, in step S5, the method for determining the number of test samples to be collected and the evaluation inspection threshold according to the type I / II error probability assessment model is as follows: the number of test samples to be collected and the evaluation inspection threshold are determined by setting the upper and lower bounds of the traversal, and the determined number of test samples and the evaluation inspection threshold must satisfy the following conditional formula: Where L(NIS,N,2α) represents the lower confidence limit of the Clopper-Pearson exact confidence interval, U(NIS,N,2β) represents the upper confidence limit of the Clopper-Pearson exact confidence interval, NIS represents the number of independent test samples, and n MD represents the number of missed samples, N represents the evaluation test threshold, the lower confidence limit is a function of NIS, N and α, and the upper confidence limit is a function of NIS, N and β. It is the equivalent missed detection rate index converted from the integrity risk index.

11. A method for evaluating the integrity of a satellite navigation application terminal with multiple fault stimuli according to claim 10, wherein the method for determining the number of test samples to be collected and the evaluation and inspection threshold by setting the upper and lower bounds of the traversal is as follows: step a) inputting the lower bound value of the number of test samples as the starting traversal value of the number of test samples; step b) traversing the evaluation and inspection threshold from 1 to the traversal value of the number of test samples; step c) during the traversal of the evaluation and inspection threshold, if the number of test samples and the evaluation and inspection threshold satisfy the conditional formula, then selecting the number of test samples and the evaluation and inspection threshold at this time and stopping the traversal; if the conditional formula is not satisfied, then increasing the number of test samples as the traversal value by a unit step length and jumping to step b); step d) if the number of test samples increases to the upper bound value and the evaluation and inspection threshold traverses from 1 to the upper bound value of the number of test samples, and still no number of test samples and the evaluation and inspection threshold satisfy the conditional formula, then relaxing the α value or the β value and jumping to step a) to start again.

12. According to claim 10, a satellite navigation application terminal multiple fault stimulus integrity assessment method, wherein the lower limit of the number of test samples required to be collected is the reciprocal value of the equivalent missed detection rate index, and the upper limit of the number of test samples required to be collected is 1 / 3 of the reciprocal value of the equivalent missed detection rate index.

13. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimulation according to claim 1, wherein in step S6, the method for determining the test location and the number of test samples at each test site is: dividing the world into K°×K° grids, randomly selecting N station The grid intersections are test sites, and the number of test samples at each test site is determined as: n single =to hide(n) MD / N station ) where n single represents the number of test samples required for each test site, ceil(·) represents the upward rounding function, n MD Indicates the number of missed samples, N station Indicates the number of test sites.

14. A method for multiple fault stimulus integrity assessment of a satellite navigation application terminal according to claim 1, wherein in step S6, the method of injecting the worst fault into the test sample used at each test location is: taking a single precision approach as a test sample, injecting the corresponding worst fault into each test sample according to the probability of occurrence of the fault mode determined in step S2, and determining the probability of occurrence of the fault mode corresponding to the worst fault; the fault injection form is a step form, and the amplitude is the worst fault amplitude.

15. According to claim 1, a satellite navigation application terminal multiple fault stimulus integrity assessment method, wherein in step S7, the method for evaluating whether the integrity risk of the satellite navigation application terminal meets expectations is: if the number of missed samples is less than or equal to the preset number of allowable missed samples, the test passes, which indicates that the integrity risk index of the satellite navigation application terminal meets expectations; otherwise, the test fails, which indicates that the integrity risk index of the satellite navigation application terminal does not meet expectations.

16. A method for evaluating the integrity of a satellite navigation application terminal under multiple fault stimuli according to claim 9, 10, 13 or 15, wherein the missed detection sample refers to a test sample in which the positioning error of the satellite navigation application terminal exceeds the warning limit of the precision approach phase and the test statistic of the integrity monitoring algorithm is lower than the corresponding test threshold, and thus the satellite navigation application terminal does not give an alarm. According to this definition, it is determined whether the collected test samples are missed detection samples and the number of missed detection samples among all test samples is counted.

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