GNSS / INS integrated navigation integrity monitoring method and system based on random sample consensus

Abstract

The application discloses a GNSS / INS integrated navigation integrity monitoring method based on random sampling consensus, which comprises the following steps: (1) applying the RANSAC algorithm to GNSS / INS integrated navigation integrity detection, which is suitable for three types of observation values, i.e., pseudo-range, carrier and Doppler, and calculating subset residual and standard deviation based on the RANSAC algorithm framework; (2) determining the number of samples, pre-screening the subset by using satellite geometric distribution, and traversing the subset to obtain all observation value residuals and standard deviations corresponding to the subset; and (3) including but not limited to calculating a test quantity by using an optimal subset, a satellite outlier proportion, a cost function and the like, and determining a test threshold by using a false alarm rate. The application is suitable for but not limited to a scene with less than four visible stars, is not limited by the assumption that error distribution is idealized Gaussian distribution, and is not affected by multi-gross error in a subset model, so that the integrity monitoring performance of GNSS / INS integrated navigation in a multi-gross error or small gross error scene can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of integrity monitoring of integrated navigation system, and particularly relates to a GNSS / INS integrated navigation integrity monitoring method and system based on random sample consensus (RANSAC). BACKGROUND

[0002] With the further development of Global Navigation Satellite System (GNSS), the number of satellites and ranging sources available for positioning will significantly increase to more than twice the current value. Therefore, it can no longer be assumed that the probability of multiple satellite failures within a certain time is negligible. The main drawback of GNSS is the dependence on good satellite visibility, so in environments where signals are disturbed (such as urban areas), on the one hand, buildings such as high-rise buildings, tunnels, viaducts, tree shadows, etc. in urban roads cause GNSS signals to be blocked, interrupted, resulting in frequent occurrence of cycle slips, reducing satellite availability, and causing satellite geometry to deteriorate, and even failing to achieve positioning due to lack of measurement information. On the other hand, buildings such as high-rise buildings and glass curtain walls also reflect satellite signals, resulting in multipath effects or non-line-of-sight effects. The frequent occurrence of these two effects can cause serious observation errors, leading to completely unreliable positioning, which is a great challenge to applications with high safety requirements (such as autonomous driving).

[0003] Integrated navigation with multiple means is the development trend of current navigation technology, and integrated navigation based on GNSS and Inertial Navigation System (INS) is one of the most widely used integrated modes, especially tight integration based on GNSS raw observations and INS data. In this integrated mode, even when there are less than 4 visible satellites, limited GNSS observation data and INS data can be effectively utilized to obtain reliable navigation information through optimal estimation algorithm for tight integration calculation.

[0004] The World Civil Aviation Organization proposed four navigation performance indicators of precision, integrity, continuity and availability in 2004 to evaluate the performance of navigation system, and the research on integrity as a measure of navigation system reliability is the key to improve navigation performance. The traditional receiver autonomous integrity monitoring (RAIM) technology mainly detects the consistency of the observation value by using redundant observation, satellite geometry configuration, measurement error mathematical characteristics and user demand information, but it is only suitable for open environment with visible star redundancy, and the measurement error distribution is idealized Gaussian assumption, which is easy to be disturbed by multiple gross errors, and the use effect is not good in complex urban environment. Therefore, a method is needed to solve the problem of GNSS / INS integrated navigation autonomous integrity monitoring in complex urban environment. SUMMARY

[0005] In order to overcome the shortcomings of the prior art, the present application provides a GNSS / INS integrated navigation integrity monitoring method and system based on random sample consensus, which uses the random sample consensus (RANSAC) algorithm as the framework, and detects faults by combining the residual error sequence of all subsets corresponding to the satellite and the theoretical standard deviation.

[0006] According to an aspect of the present application, a GNSS / INS integrated navigation integrity monitoring method based on random sample consensus is provided, comprising:

[0007] Let the sampling number be m, use m satellite observation values in the subset to update the measurement, and get the navigation result of the subset;

[0008] According to the navigation result calculated in the subset, the observation value residual error and the corresponding residual error standard deviation of the satellite outside the subset are calculated one by one;

[0009] When all subsets are traversed, the random sample consensus algorithm is used as the framework, and the observation value residual error and the corresponding residual error standard deviation of the satellite outside the subset are used to determine the gross error of each satellite, and the integrity monitoring result is formed.

[0010] As a further technical solution, the random sample consensus algorithm is used as the framework, and the observation value residual error and the corresponding residual error standard deviation of the satellite outside the subset are used to determine the gross error of each satellite, comprising:

[0011] The cost function is used to calculate the test quantity of different satellites, and the test quantity is compared with the detection threshold, and when the test quantity is greater than the detection threshold, it is determined that the current satellite belongs to the gross error.

[0012] As a further technical solution, in the framework of the random sample consensus algorithm, the observation value residual of the satellite outside the subset and the standard deviation of the corresponding residual are used to determine the gross error of each satellite, including:

[0013] The residual of the observation value of the satellite outside the subset and the standard deviation of the observation value are used as the test quantity, compared with the detection threshold, and the satellite less than the detection threshold is defined as the inner point of the current subset, the subset with the most inner points is selected as the optimal subset, and the outer point in the optimal subset is identified as the gross error.

[0014] As a further technical solution, in the framework of the random sample consensus algorithm, the observation value residual of the satellite outside the subset and the standard deviation of the corresponding residual are used to determine the gross error of each satellite, including:

[0015] The ratio of the observation value residual and the standard deviation of the satellite outside the subset is used as the test quantity to distinguish the inner and outer points, and the number of times each satellite is divided into an outer point is recorded while traversing the subset, and after all subsets are traversed, whether the satellite belongs to the gross error is determined according to the proportion of the number of times each satellite is divided into an outer point.

[0016] As a further technical solution, the method further comprises:

[0017] The number of samples m is determined to be 2, and the angular distance between two-star subsets is used to measure the geometric distribution of the satellites to screen the subsets.

[0018] As a further technical solution, the observation value residual of the satellite outside the subset and the standard deviation of the corresponding residual are calculated, including: pseudo-range, carrier or Doppler is used as the observation value for calculation.

[0019] As a further technical solution, the method further comprises: determining the detection threshold according to the false alarm rate.

[0020] According to one aspect of the present application, a GNSS / INS integrated navigation integrity monitoring system based on random sample consensus is provided, comprising:

[0021] A first main module is used to perform measurement update using the observation values in the m-star subset to obtain the navigation results of the subset;

[0022] A second main module is used to calculate the observation value residual of the satellite outside the subset and the standard deviation of the corresponding residual according to the navigation results of the subset;

[0023] A third main module is used to determine the gross error of each satellite according to the observation value residual of the satellite outside the subset and the standard deviation of the corresponding residual in the framework of the random sample consensus when all subsets are traversed, and form an integrity monitoring result.

[0024] According to an aspect of the present application, a GNSS / INS integrated navigation integrity monitoring device based on random sample consensus is provided, comprising a memory and a processor; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sample consensus.

[0025] According to an aspect of the present application, a non-transitory computer readable storage medium is provided, which stores computer instructions, and the computer instructions make the computer execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sample consensus.

[0026] Compared with the prior art, the present application has the following beneficial effects:

[0027] 1. The present application is based on the RANSAC algorithm framework to calculate the subset residual and test quantity for the GNSS / INS integrated navigation system, and the fault detection depends on the consistency between the observation values, which is suitable for the scene with less than 4 visible stars compared with the traditional RAIM algorithm, is not limited by the assumption that the error distribution is ideal Gaussian distribution, the subset model is more accurate, and the integrity detection performance in the multi-gross error or small gross error scene can be effectively improved.

[0028] 2. The present application reduces the number of subsets by a minimum sampling number and by satellite angular distance screening, and does not need to balance the algorithm efficiency and accuracy like the traditional RANSAC algorithm.

[0029] 3. The present application processes the vehicle-mounted measured simulation gross error result to verify the present application: in the multi-gross error and small gross error scene, the fault can be correctly detected. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 A flow chart of the GNSS / INS integrated navigation integrity monitoring method based on random sample consensus provided by the embodiments of the present application is provided.

[0032] Figure 2 A carrier residual and its theoretical standard deviation time series diagram of GPS satellite No. 20 (normal satellite) with a 0.5-week carrier error mode under the simulation of 3 satellites provided by the embodiments of the present application are provided.

[0033] Figure 3 A simulation 3-satellite with 546953 epochs of fault detection results under 0.5 week carrier error mode provided for the embodiment of the present application is shown in the schematic diagram;

[0034] Figure 4 The structure schematic diagram of the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided for the embodiment of the present application is shown in the schematic diagram;

[0035] Figure 5 The structure schematic diagram of the GNSS / INS integrated navigation integrity monitoring device based on random sample consensus provided for the embodiment of the present application is shown in the schematic diagram. DETAILED DESCRIPTION

[0036] It should be noted that:

[0037] The present application aims at the problem that the existing GNSS / INS integrated navigation system integrity monitoring method has poor use effect under the condition of insufficient number of visible satellites and multiple gross errors, and proposes a GNSS / INS integrated navigation integrity monitoring method based on random sample consensus (RANSAC) algorithm, which uses cost function, optimal subset, satellite outlier ratio and other methods to comprehensively detect faults based on the sequence of residual errors and theoretical standard deviation of all subsets corresponding to the satellites.

[0038] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application. In addition, the technical features in each embodiment or single embodiment provided by the present application can be combined with each other to form new technical scheme, and such combination is not restricted by the order of steps and / or structure composition mode, but should be based on the realization by those skilled in the art, when the combination of technical scheme appears contradictory or unachievable, it should be considered that the combination of technical scheme does not exist, and is not within the protection scope required by the present application.

[0039] The embodiment of the application provides a GNSS / INS integrated navigation integrity monitoring method based on random sample consensus, comprising: performing measurement updating by using observation values in an m-star subset to obtain navigation results of the subset; according to the navigation results of the subset, iteratively calculating observation value residuals of satellites outside the subset and standard deviations of the corresponding residuals; when the iteration of all subsets is completed, performing coarse error determination on each satellite according to the observation value residuals of the satellites outside the subset and the standard deviations of the corresponding residuals based on a random sample consensus framework to form an integrity monitoring result.

[0040] It should be noted that GNSS observation values include three types of pseudo-range, carrier and Doppler, and the samples in the subset only contain the types of observation values to be measured, and the embodiment of the application takes the carrier as an example to describe the calculation process of the fault detection test quantity, and the related calculation is also applicable to pseudo-range and Doppler observation values.

[0041] Preferably, the embodiment of the application takes the method of calculating the cost function as an example to perform coarse error determination on each satellite, but the application supports different methods such as methods (1), (2) and (3) based on the RANSAC framework.

[0042] (1) Comparing the residual of the observation value outside the subset with a threshold value, the threshold value is a multiple of the theoretical standard deviation of the residual, and the satellites less than the threshold value are defined as inliers of the current subset, and then the subset with the most inliers is selected as the optimal subset, and the outliers in the optimal subset are identified as coarse errors.

[0043] (2) The ratio of the residual outside the subset to the standard deviation is used as a test quantity to distinguish inliers and outliers, and the number of times that each satellite is divided into an outlier is recorded while iterating the subset, and after the iteration of all subsets is completed, whether the satellite belongs to a coarse error is judged according to the proportion of the number of times that the satellite is divided into an outlier.

[0044] (3) The observation values of different satellites are calculated by using a cost function, and compared with a threshold value to determine whether the observation values of the satellites belong to coarse errors.

[0045] The embodiment of the application provides a GNSS / INS integrated navigation integrity monitoring method based on random sample consensus, wherein the process of selecting a subset sample is as follows:

[0046] The sampling number is the minimum number of samples required for solving model parameters, and in the context of integrated navigation, it represents the minimum number of satellites required for positioning. In GNSS resolution, at least 4 satellites are required for positioning resolution. In GNSS / INS tight integration, the assistance of INS is added, and less than 4 satellites can also be used for positioning, and when 2 satellites satisfy a good geometric distribution, they can play a good auxiliary role. Therefore, the sampling number is preferably m=2.

[0047] Unlike computer vision, when RANSAC is used for gross error detection, there is no need to set an upper limit on iterations, because the number of 2-star subsets participating in the test at each epoch is small, and in order to ensure the accuracy of the subset model, the geometric distribution of the satellites is required to be reasonable, so the subset needs to be screened according to the satellite angular distance, and the angular distance of the subset sample satellites is controlled within a certain range.

[0048] The subset residual error calculation in the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by the embodiment of the application specifically includes:

[0049] The observation equation of the carrier is:

[0050]

[0051] wherein, is the actual carrier observation value, the subscript i represents a receiver, and the superscript j represents a satellite; λ i is a carrier wavelength; is a geometric distance from the receiver i to the satellite j; and are correction numbers of receiver clock errors and satellite clock errors respectively, and the unit is meter; and are correction numbers of ionospheric errors and tropospheric errors respectively; is an integer ambiguity, and the unit is cycle. It is noted that the correction number is equal in size and opposite in sign to the error.

[0052] The inter-station single-difference observation value is:

[0053]

[0054] wherein, the subscript r represents a rover, and the subscript b represents a base station. When the distance between the rover and the base station is less than 15 km, the inter-station single-difference can eliminate the satellite-end errors and the errors with strong geometric correlation, so the satellite clock error, the ionospheric error and the tropospheric error are eliminated after being differentiated. Thus, the single-difference observation equation is further obtained:

[0055]

[0056] wherein, is a single-difference geometric distance; is a correction number of a single-difference clock error; is a single-difference ambiguity.

[0057] If the carrier residual error is constructed based on formula (3), the single-difference carrier observation value of the satellite outside the subset needs to be calculated through the navigation result of the tight combination of the subset, that is:

[0058]

[0059] In the above formula, And Can be obtained by model parameters, but Is unknown, because the solution of the tight combination of the subset only contains the single difference ambiguity of the subset satellite, and the ambiguity of the satellite outside the subset is unknown. Therefore, it is necessary to further eliminate the single difference ambiguity.

[0060] Normally, the ambiguity is the same at the front and rear epochs. The inter-epoch difference of formula (3) can eliminate the ambiguity:

[0061]

[0062] The carrier double difference based on the navigation result of the 2-star subset tight combination is:

[0063]

[0064]

[0065] Wherein, ||r r (t)-r j (t)|| and ||r b (t)-r j (t)|| are the distances from the rover station and the base station to the satellite, and the calculation formula is:

[0066]

[0067] Wherein, X i ,Y i ,Z i are the three-dimensional coordinates of the receiver; X j ,Y j ,Z j are the three-dimensional coordinates of the satellite.

[0068] Subtracting formula (6) from formula (5), the subset carrier residual error

[0069]

[0070] The subset residual error variance calculation in the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by the embodiment of the application specifically includes:

[0071] Let The variance of The variance of According to the error propagation law, the variance of the residual error is:

[0072]

[0073] a) Calculate the variance of

[0074] According to formula (7) (8),

[0075]

[0076] Linearize the formula and expand it into a matrix multiplication form:

[0077]

[0078] Wherein, X r0 , Y r0 , Z r0 are the true values of the flow station position; x r =X r -X r0 , y r =Y r -Y r0 , z r =Z r -Z r0 are the position errors of the flow station;

[0079] Let [delta x r , delta y r , delta z r ] T The variance covariance matrix is According to the error propagation law, The variance of is:

[0080]

[0081] In the embodiment of the application, the position error is defined as the position error of the inertial navigation in the n system The variance is Further converted into Let the variance of the attitude error be D φ , then The variance of is:

[0082]

[0083] Wherein, H φ is the design matrix of the carrier observation value when the tight combination is measured and updated.

[0084] Let the coordinates in the n system and the coordinates in the e system have the following conversion formula: The position error in the e system is:

[0085]

[0086] Then The variance of is:

[0087]

[0088] b) Calculate the variance of Let

[0089] And The variance of and respectively is Let And The variance of and respectively is Then The variance of is:

[0090]

[0091] Wherein, and are the single-difference receiver clock error variance at t2 and t1 respectively, which can be obtained by the diagonal elements of the Kalman filter P matrix; Can be calculated by formula (13).

[0092] c) Calculate the variance of According to formula (2) and formula (5), the variance of can be obtained as:

[0093]

[0094]

[0095] Wherein, and are the carrier observation noise variance of the satellite j at t2 and t1 respectively by the rover station and the base station, And are the carrier observation noise variance of the satellite j at t1 respectively by the rover station and the base station.

[0096] The method for calculating the cost function is taken as an example in the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by the embodiment of the application, and the fault detection process specifically includes:

[0097] 1. Test quantity calculation.

[0098] Let the residual vector of satellite j in the non-sample subset be, wherein n is the total number of residuals:

[0099]

[0100] The corresponding residual vector standard deviation is:

[0101] ​​​​​

[0102] The corresponding ratio vector is:

[0103]

[0104] Let the test quantity of satellite j be S j The calculation of the test quantity of satellite j is as follows:

[0105]

[0106] Where K(ratio j ) is a selected kernel function, and the present application takes four kernel functions as examples, but the present application is applicable to other kernel functions, which will not be described here. The four kernel functions are: mean absolute error (MAE), mean squared error (MSE), exponential function (EXP), and logarithmic hyperbolic cosine (LOG-COSH). The function expression of the test quantity of satellite j is as follows:

[0107]

[0108] The fault detection threshold calculation specifically includes:

[0109] Taking the MSE function as an example, according to statistics, obeys the chi-square distribution with N degrees of freedom.

[0110] The following hypothesis test is made, where the null hypothesis and the alternative hypothesis can be set as:

[0111] H0: Assume no fault.

[0112] H1: Assume there is a fault.

[0113] When the satellite observation value is fault-free (H0), the system is in a normal monitoring state at this time, and according to statistics, S j obeys the chi-square distribution with N degrees of freedom. The false alarm rate refers to the probability that the system is judged to be abnormal without a fault, and the false alarm rate is denoted as P FA , and the following expression is obtained:

[0114]

[0115] Where χ 2 (x, N) is the probability density function of the chi-square distribution, and N is the degree of freedom. Under the condition of a given false alarm rate, the fault detection threshold T can be solved based on the above formula.

[0116] Figure 1 The flow of the fault detection test quantity calculation based on RANSAC is given. Block ① shows the operation that each subset needs to perform: first, the measurement update of filtering is performed using the observation values in the 2-star subset to obtain the navigation result of the subset; then, the double-difference carrier residual of the satellite outside the subset is calculated according to formula (9), and the standard deviation of the corresponding residual is calculated according to formula (10); after the traversal of all subsets is completed, the algorithm flow enters block ②, and each satellite is judged in turn: the test quantity of each satellite is calculated according to formula (23), the corresponding threshold T is calculated according to formula (24) and the false alarm rate P FA The corresponding threshold T is calculated, and compared with the threshold T. If it is greater than the threshold, it is considered that the satellite observation value belongs to the gross error.

[0117] The embodiment of the application is realized and verified based on a PC terminal, and reference is made to Figure 2 and Figure 3 .

[0118] Figure 2 The double-difference carrier residual and the standard deviation of the GPS 20 satellite (normal satellite) of the low-end MEMSIMU (ICM-20602) in the simulation of 3 satellites with a 0.5-week carrier error mode are given. The residual sequence given in the figure is the total residual of each subset of the GPS 20 satellite at each epoch, for example, assuming that there are 20 2-star subsets at an epoch, and the subset constructed by the GPS 20 satellite has 5, then 20-5=15 GPS 20 satellite residuals can be calculated at the epoch, Figure 2 The results of multiple epochs are shown on the basis of the above. Figure 2 The data shown are collected in an open environment, the carrier observation noise is set to 0.01 weeks, and the position error STD given by the P matrix is usually less than 5 cm. At this time, the residual theoretical standard deviation calculated according to formula (20) is about 0.3 weeks, corresponding to the orange curve in the figure. The data simulates a 0.5-week cycle slip, which is reflected on the double-difference carrier residual as follows: 1) when the 2-star subset does not contain an abnormal satellite, the residual sequence calculated by the subset has a residual of the normal satellite <0.5 weeks and a residual of the abnormal satellite >0.5 weeks; 2) when the 2-star subset contains an abnormal satellite, the residual sequence calculated by the subset has a residual of the normal satellite and the abnormal satellite >0.5 weeks; 3) the residual spike part is close to the size of the error.

[0119] Figure 3Fig. 1 is a schematic diagram of fault detection results in the simulation mode. There are 13 satellites in the epoch, and the numbers of the 3 abnormal satellites in the simulation are C14, G25 and G31 respectively. There are 19 subsets after screening the 2-star subsets formed by the 13 satellites. The value in each block in the figure represents the ratio of the residual of the satellite in the subset to the standard deviation, and -1 represents that the satellite is a sample in the subset. The value after the satellite number in each column represents the ratio of the test quantity to the threshold value. If it is greater than 1, it means that the observation value of the satellite is determined to be a gross error. As can be seen from the figure, the results are consistent with the simulation.

[0120] The implementation basis of each embodiment of the present application is realized by a programmed process of a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present application are packaged into various modules. Based on this actual situation, on the basis of the above-mentioned embodiments, the embodiments of the present application provide a GNSS / INS integrated navigation integrity monitoring system based on random sampling consensus, which is used to execute a GNSS / INS integrated navigation integrity monitoring method based on random sampling consensus in the above-mentioned method embodiments.

[0121] Referring to Figure 4 , the system comprises: a first main module, configured to perform measurement updating by using the observation values in the m-star subset to obtain the navigation result of the subset; a second main module, configured to traverse and calculate the observation value residual and the corresponding residual standard deviation of the satellite outside the subset according to the navigation result of the subset; and a third main module, configured to perform gross error determination on each satellite according to the observation value residual and the corresponding residual standard deviation of the satellite outside the subset based on the framework of random sampling consensus after the traversal of all the subsets is completed, to form the integrity monitoring result.

[0122] The GNSS / INS integrated navigation integrity monitoring system based on random sampling consensus provided by the embodiments of the present application is used to solve the problem that the existing GNSS / INS integrated navigation system integrity monitoring method has poor use effect in the condition of insufficient number of visible satellites and multiple gross errors. Figure 4 The system comprises several modules in the RANSAC algorithm framework to calculate the subset residual and the test quantity, and the fault detection depends on the consistency between the observation values. The subset model is not affected by multiple gross errors. Compared with the traditional RAIM algorithm, the system is applicable to the scene of less than 4 visible stars, is not limited by the assumption that the error distribution is ideal Gaussian distribution, the subset model is more accurate, and the integrity detection performance in the multiple gross error or small gross error scene can be effectively improved.

[0123] It should be noted that the system embodiments provided by the present application are used to implement the methods in the above method embodiments, and are also used to implement the methods in other method embodiments provided by the present application, the difference is only that the corresponding function modules are set, and the principle is basically the same as that of the above system embodiments provided by the present application, as long as the person skilled in the art improves the modules in the above system embodiments on the basis of the above system embodiments, refers to the specific technical solutions in other method embodiments, obtains the corresponding technical means by combining technical features, and the technical solutions composed of these technical means, on the premise of ensuring the practicability of the technical solutions, the corresponding system class embodiments are obtained, which are used to implement the methods in other method class embodiments. For example:

[0124] Based on the content of the above system embodiment, as a preferred embodiment, the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided in the embodiment of the present application, the third main module is further used to execute the following instructions:

[0125] The cost function is used to calculate the test quantity of different satellites, and the test quantity is compared with the detection threshold value, and when the test quantity is greater than the detection threshold value, it is determined that the current satellite belongs to the gross error.

[0126] Based on the content of the above system embodiment, as a preferred embodiment, the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided in the embodiment of the present application, the third main module is further used to execute the following instructions:

[0127] The residual error of the observation value of the subset is used as the test quantity, and compared with the detection threshold value, the satellite less than the detection threshold value is defined as the inlier of the current subset, the subset with the most inliers is selected as the optimal subset, and the outliers in the optimal subset are identified as the gross error.

[0128] Based on the content of the above system embodiment, as a preferred embodiment, the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided in the embodiment of the present application, the third main module is further used to execute the following instructions:

[0129] The ratio of the observation value residual error and the standard deviation of the satellite outside the subset is used as the test quantity to distinguish the inliers and outliers, and the number of times that each satellite is divided into an outlier is recorded while traversing the subset, after all the subsets are traversed, whether the satellite belongs to the gross error is judged according to the proportion of the number of times that each satellite is divided into an outlier.

[0130] Based on the content of the above system embodiment, as a preferred embodiment, the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided in the embodiment of the application further comprises a screening module configured to determine the sampling number m and screen the subsets by using satellite geometric distribution.

[0131] Based on the content of the above system embodiment, as a preferred embodiment, the GNSS / INS integrated navigation integrity monitoring system based on random sample consensus provided in the embodiment of the application further comprises a threshold determination module configured to determine the detection threshold according to the false alarm rate.

[0132] The embodiment of the application further provides a GNSS / INS integrated navigation integrity monitoring device based on random sample consensus, which comprises a memory and a processor; the memory stores program instructions executed by the processor, and the processor invokes the program instructions to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sample consensus. Figure 5 As shown in the figure, the monitoring device comprises at least one processor, a communications interface, at least one memory and a communications bus, wherein the at least one processor, the communications interface and the at least one memory complete mutual communication through the communications bus. The at least one processor invokes the logic instructions in the at least one memory to execute all or part of the steps of the method provided by each method embodiment.

[0133] The embodiment of the application further provides a non-transitory computer readable storage medium, which stores computer instructions, and the computer instructions make the computer execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sample consensus.

[0134] The logic instructions in the at least one memory described above are implemented in the form of software functional units and sold or used as independent products when the logic instructions are stored in a computer readable storage medium. Based on such understanding, the technical solutions of the application essentially or the part of the technical solutions that make contributions to the prior art or the part of the technical solutions in the form of software products are embodied, the computer software product is stored in a storage medium, and includes a plurality of instructions to make a computer device (a personal computer, a server or a network device) execute all or part of the steps of the method described in each method embodiment of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk various storage program code medium.

[0135] The system embodiments described above are merely illustrative, wherein the units described as separate components are or are not physically separated, the components displayed as units are or are not physical units, located in one place, or also distributed to multiple network units. According to the actual selection of part or all of the modules, the purpose of the embodiment scheme is achieved. Those skilled in the art can understand and implement without creative labor.

[0136] In summary, the application discloses a GNSS / INS integrated navigation integrity monitoring method based on random sampling consensus, including (1) applying the RANSAC algorithm to GNSS / INS integrated navigation integrity detection, suitable for three types of observation values of pseudo-range, carrier and Doppler, calculating subset residual and standard deviation based on the RANSAC algorithm framework; (2) determining the number of samples, pre-screening the subset using satellite geometric distribution, and traversing the subset to obtain all corresponding observation value residuals and standard deviations; (3) including but not limited to calculating the test quantity using the optimal subset, satellite outlier ratio, cost function and other methods, and determining the test threshold using the false alarm rate.

[0137] The application is suitable for the scene of less than 4 visible stars, is not limited by the assumption that the error distribution is ideal Gaussian distribution, the subset model is not affected by multi-coarse error, and the integrity detection performance of GNSS / INS integrated navigation in a multi-coarse error or small coarse error scene can be effectively improved.

[0138] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the application.

Claims

1. A GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency is characterized by: Applicable to scenes with less than 4 visible stars, including: The observation values ​​in the m-satellite subset are used to perform measurement updates to obtain the navigation results of the subset; wherein, the sampling number m is determined to be 2, and the subset is screened using the satellite angular distance; Based on the GNSS / INS integrated navigation results of the subset, traversing and calculating the observation value residuals of satellites outside the subset and the standard deviations of the corresponding residuals one by one, including: calculating using pseudorange, carrier or Doppler as the observation value; After all subsets are traversed, the random sampling consistency is used as a framework to determine the gross error of each satellite based on the observation residuals of satellites outside the subset and the standard deviation of the corresponding residuals. This generates the observation gross error detection results, providing a good environment for solving the integrity parameters.

2. The GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to claim 1, characterized in that: Based on the random sampling consistency framework, the gross error determination is performed for each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including: The cost function is used to calculate the inspection amount of different satellites, and the inspection amount is compared with the detection threshold. When the inspection amount is greater than the detection threshold, it is determined that the current satellite belongs to a gross error.

3. The GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to claim 1, characterized in that: Based on the random sampling consistency framework, the gross error determination is performed for each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including: The residuals of the observations outside the subset and the standard deviation of the observations are used as test quantities and compared with the detection threshold. Satellites with values ​​less than the detection threshold are defined as inliers of the current subset. The subset with the largest number of inliers is selected as the optimal subset, and the outliers in the optimal subset are identified as gross errors.

4. The GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to claim 1, characterized in that: Based on the random sampling consistency framework, the gross error determination is performed for each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including: The ratio of the residual error of the observation value of satellites outside the subset to the standard deviation is used as a test quantity to distinguish the inliers and outliers. While traversing the subset, the number of times each satellite is classified as an outlier is recorded. After all subsets are traversed, the proportion of the number of outliers for each satellite is used to determine whether the satellite is a gross error.

5. The GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to claim 2 or 3, characterized in that: The method further includes determining a detection threshold according to a false alarm rate.

6. A GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency, characterized by: Applicable to scenes with less than 4 visible stars, including: The first main module is used to perform measurement updates using observation values ​​from the m-satellite subset to obtain navigation results for the subset; wherein the sampling number m is determined to be 2, and the subset is screened using the satellite angular distance; The second main module is used to traverse and calculate the observation value residuals and the standard deviations of the corresponding residuals of the satellites outside the subset one by one according to the navigation results of the subset, including: using pseudorange, carrier or Doppler as the observation value for calculation; The third main module is used to determine the gross error of each satellite based on the observation residuals of satellites outside the subset and the standard deviation of the corresponding residuals after all subsets are traversed, using random sampling consistency as a framework to form an integrity monitoring result.

7. GNSS / INS integrated navigation integrity monitoring equipment based on random sampling consistency, characterized by: The invention comprises a memory and a processor; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which enable the computer to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to any one of claims 1 to 5.

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