GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) integrated navigation integrity monitoring method and system based on random sampling consistency

Abstract

The invention discloses a GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) integrated navigation integrity monitoring method based on random sampling consistency, which comprises the following steps of: (1) applying an RANSAC (Random Sample Consensus) algorithm to GNSS / INS integrated navigation integrity detection which is suitable for observation values of three types of pseudo range, carrier wave and Doppler, and calculating a subset residual error and a standard deviation based on an RANSAC algorithm framework; (2) determining a sampling number, pre-screening subsets by utilizing satellite geometric distribution, and traversing the subsets to obtain residual errors and standard deviations of all corresponding observation values; and (3) calculating the inspection amount by using methods including but not limited to an optimal subset, a satellite exterior point proportion, a cost function and the like, and determining an inspection threshold by using a false alarm rate. The method is suitable for but not limited to a scene with less than four visible stars, is not limited by a hypothesis that error distribution is idealized Gaussian distribution, is not influenced by multiple gross errors, and can effectively improve the integrity monitoring performance of GNSS / INS integrated navigation in a multi-gross-error or small-gross-error scene.

Description

Technical Field

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

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

[0003] Combined navigation of multiple means is the development trend of current navigation technology. Combined navigation based on GNSS and inertial navigation system (INS) is one of the most widely used combination modes, especially the tight combination based on GNSS original observations and INS data. In this combination mode, even when there are less than 4 visible satellites, limited GNSS observation data and INS data can be effectively used to perform tight combination and solution through the optimal estimation algorithm to obtain reliable navigation information.

[0004] In 2004, the International Civil Aviation Organization proposed four navigation performance indicators, namely accuracy, integrity, continuity and availability, to evaluate the performance of navigation systems. The study of integrity, which was proposed as a measure of the reliability of navigation systems, is the key to improving navigation performance. Traditional receiver autonomous integrity monitoring (RAIM) technology mainly uses information such as redundant observations, satellite geometry, mathematical characteristics of measurement errors and user needs to detect the consistency of observations. However, it is only applicable to open environments with visible satellite redundancy, and makes an idealized Gaussian assumption on the distribution of measurement errors, which is easily interfered by multiple gross errors and has poor performance in complex urban environments. Therefore, a method is needed to solve the problem of autonomous integrity monitoring of GNSS / INS integrated navigation in complex urban environments. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a GNSS / INS integrated navigation integrity monitoring method and system based on random sampling consistency, which uses the random sampling consistency (RANSAC) algorithm as the framework to perform fault detection on the sequence of residuals and theoretical standard deviations of all subsets corresponding to the integrated satellite.

[0006] According to one aspect of the present invention, a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency is provided, comprising:

[0007] The sampling number is recorded as m, and the m satellite observation values ​​in the subset are used to perform measurement updates to obtain the navigation results of the subset;

[0008] According to the navigation results solved in the subset, the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals are traversed and calculated one by one;

[0009] When all subsets are traversed, the random sampling consensus algorithm is used as a framework to determine the gross error of each satellite based on the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals to form an integrity monitoring result.

[0010] As a further technical solution, a random sampling consensus algorithm is used as a framework to determine the gross error of each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including:

[0011] 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.

[0012] As a further technical solution, a random sampling consensus algorithm is used as a framework to determine the gross error of each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including:

[0013] 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 smaller than the detection threshold are defined as the 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.

[0014] As a further technical solution, a random sampling consensus algorithm is used as a framework to determine the gross error of each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, including:

[0015] The ratio of the residual and standard deviation of the observation value of the satellite outside the subset is used as a test quantity to distinguish the inner and outer points. While traversing the subset, the number of times each satellite is classified as an outer point is recorded. After all subsets are traversed, the proportion of the number of outer points of each satellite is used to determine whether the satellite is a gross error.

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

[0017] The sampling number m is determined to be 2, and the satellite geometric distribution is measured by using the angular distance between the two satellite subsets to screen the subsets.

[0018] As a further technical solution, the residuals of the observation values ​​of the satellites outside the subset and the standard deviations of the corresponding residuals are calculated, including: using pseudorange, carrier or Doppler as the observation value for calculation.

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

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

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

[0022] The second main module is used to traverse and calculate the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals one by one according to the navigation results of the subset;

[0023] The third main module is used to determine the gross error of each satellite based on the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals after all subsets are traversed, so as to form an integrity monitoring result.

[0024] According to one aspect of the present invention specification, a GNSS / INS integrated navigation integrity monitoring device based on random sampling consistency 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 sampling consistency.

[0025] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency.

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

[0027] 1. The present invention is aimed at the GNSS / INS integrated navigation system, and calculates subset residuals and test quantities based on the RANSAC algorithm framework. Fault detection relies on the consistency between observations. Compared with the traditional RAIM algorithm, it is suitable for scenarios with less than 4 visible satellites, and is not restricted by the assumption that the error distribution is an idealized Gaussian distribution. The subset model is more accurate and can effectively improve the integrity detection performance in scenarios with multiple gross errors or small gross errors.

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

[0029] 3. The present invention verifies the present invention by processing the results of on-board simulated gross errors: faults can be correctly detected in scenarios with multiple gross errors and small gross errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 A flow chart of a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided in an embodiment of the present invention;

[0032] Figure 2 A timing diagram of the carrier residual and its theoretical standard deviation of GPS20 satellite (normal satellite) in a simulation mode of 3 satellites with 0.5-cycle carrier error provided by an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of fault detection results of 546953 epochs in a simulated 3-satellite with 0.5-cycle carrier error mode provided by an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of the structure of a GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency provided by an embodiment of the present invention;

[0035] Figure 5 A schematic diagram of the structure of a GNSS / INS integrated navigation integrity monitoring device based on random sampling consistency provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] It should be noted that:

[0037] Aiming at the problem that the existing GNSS / INS integrated navigation system integrity monitoring method has poor effect in scenarios with insufficient number of visible satellites and multiple gross errors, the present invention proposes a GNSS / INS integrated navigation integrity monitoring method based on random sampling consensus (RANSAC) algorithm. Based on the RANSAC algorithm, the method uses cost function, optimal subset, satellite outlier ratio and other methods to comprehensively detect faults by using the residuals of all subsets corresponding to the satellite and the sequence of theoretical standard deviations.

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0039] The embodiment of the present invention provides a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency, comprising: using observation values ​​in an m-satellite subset to perform measurement update to obtain a navigation result of the subset; traversing and calculating the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals one by one according to the navigation result of the subset; after the traversal of all subsets is completed, using random sampling consistency as a framework, according to the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals, performing gross error judgment on each satellite to form an integrity monitoring result.

[0040] It should be noted that GNSS observation values ​​include three types: pseudorange, carrier, and Doppler. The samples in the subset only include the type of observation value to be measured. The embodiment of the present invention takes the carrier as an example to illustrate the calculation process of the fault detection test quantity. The relevant calculation is also applicable to pseudorange and Doppler observation values.

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

[0042] (1) The residual of the observation value outside the subset is compared with the threshold. The threshold is a multiple of the theoretical standard deviation of the residual. The satellites with a value less than the threshold are defined as the inliers of the current subset. Then, the subset with the largest number of inliers is selected as the optimal subset. The outliers in the optimal subset are identified as gross errors.

[0043] (2) The ratio of the subset outlier residual to the standard deviation is used as a test quantity to distinguish inliers and outliers. While traversing the subsets, the number of times each satellite is classified as an outlier is recorded. After all subsets are traversed, the proportion of outlier times for each satellite is used to determine whether the satellite is a gross error.

[0044] (3) Use the cost function to calculate different satellite observation values ​​and compare them with the threshold to determine whether the satellite observation value is a gross error.

[0045] In a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by an embodiment of the present invention, the process of selecting subset samples is as follows:

[0046] The sampling number is the minimum number of samples required to solve the model parameters. In the scenario of integrated navigation, it indicates the minimum number of satellites required for positioning. In GNSS solution, at least 4 satellites are required for positioning solution. In the GNSS / INS tight combination, with the addition of INS assistance, positioning can be performed with less than 4 satellites. When 2 satellites meet a good geometric distribution, they can play a better 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 iteration upper limit. Since the number of 2-satellite subsets participating in the test in each epoch is small, and in order to ensure the accuracy of the subset model, the geometric distribution of satellites is required to be reasonable, it is necessary to screen the subset according to the satellite angular distance and control the angular distance of the subset sample satellites within a certain range.

[0048] In a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by an embodiment of the present invention, the calculation of subset residuals specifically includes:

[0049] The observation equation of the carrier is:

[0050]

[0051] in, is the actual carrier observation value, the subscript i represents the receiver, and the superscript j represents the satellite; λ i is the carrier wavelength; is the geometric distance from receiver i to satellite j; and are the corrections of the receiver clock error and the satellite clock error, respectively, in meters; and are the corrections for ionospheric and tropospheric errors, respectively; is the integer ambiguity, in weeks. Note that the correction is equal to the error, but has the opposite sign.

[0052] The single-difference observations between stations are:

[0053]

[0054] Where, subscript r represents the rover; subscript b represents the base. When the distance between the rover and the base station is less than 15 km, the single difference between the stations can eliminate the satellite errors and errors with strong geometric correlation. Therefore, the satellite clock error, ionospheric error and tropospheric error are eliminated after difference. Thus, the single difference observation equation is further obtained:

[0055]

[0056] in, is the single difference geometric distance; is the correction number of the single difference clock error; is the single difference ambiguity.

[0057] If the carrier residual 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 results of the subset tight combination, that is:

[0058]

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

[0060] Under normal circumstances, the ambiguity is the same between the two epochs. The ambiguity can be eliminated by performing inter-epoch differences on equation (3):

[0061]

[0062] The carrier double difference inferred from the navigation result based on the tight combination of two satellite subsets is:

[0063]

[0064]

[0065] Among them, ||r r (t)-r j (t)|| and||r b (t)-r j (t)|| are the distances from the mobile station and base station to the satellite, respectively. The calculation formula is:

[0066]

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

[0068] Subtracting equation (6) from equation (5) yields the subset carrier residual:

[0069]

[0070] In a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by an embodiment of the present invention, the subset residual variance calculation specifically includes:

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

[0072]

[0073] a) Calculation Variance

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

[0075]

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

[0077]

[0078] Among them, X r0 ,Y r0 ,Z r0 is the true value of the mobile station position; x r =X r -X r0 ,y r =Y r -Y r0 ,z r =Z r -Z r0 is the position error of the rover;

[0079] remember [δx r δy r δz r ] T The variance-covariance matrix of According to the error propagation law, The variance of is:

[0080]

[0081] In the embodiment of the present invention, the position error is defined as the position error of the inertial navigation in the n-frame: The variance is Need to be further transformed into The variance of the attitude error is recorded as D φ ,but The variance of is:

[0082]

[0083] Among them, H φ It is the design matrix when the carrier observation values ​​are tightly combined for measurement update.

[0084] 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] but The variance of is:

[0087]

[0088] b) Calculation Variance

[0089] remember and The variances of remember and The variances of but The variance of is:

[0090]

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

[0092] c) Calculation Variance

[0093] According to formula (2) and formula (5), we can get The variance of is:

[0094]

[0095] in, are the carrier observation noise variances of the mobile station and the base station to satellite j at time t2, are the carrier observation noise variances of the mobile station and the base station on satellite j at time t1 respectively.

[0096] In a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by an embodiment of the present invention, taking a method for calculating a cost function as an example, the fault detection process specifically includes:

[0097] 1. Calculation of inspection quantity.

[0098] Assume that the residual vector of satellite j in the non-sample subset is, where 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 , then the calculation of the satellite j verification quantity is as follows:

[0105]

[0106] Among them, K(ratio j ) is the selected kernel function. The present invention takes four kernel functions as examples, but the present invention is applicable to other kernel functions and will not be described here. The four kernel functions are: Mean Absolute Error (MAE), Mean Squared Error (MSE), Exponential (EXP), Log-Hyperbolic Cosine (LOG-COSH), then the function expression of the satellite j test quantity is as follows:

[0107]

[0108] In a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency provided by an embodiment of the present invention, the fault detection threshold calculation specifically includes:

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

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

[0111] H0: Assume there is 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. According to statistics, S j It obeys the chi-square distribution with N degrees of freedom. The false alarm rate refers to the probability that the system is judged abnormal when there is no failure. The false alarm rate is P FA , then we have the following expression:

[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 obtained based on the above formula.

[0116] Figure 1 The process of calculating the fault detection test quantity based on RANSAC is given. Box ① shows the operations that need to be performed on each subset: first, the observation values ​​in the 2-satellite subset are used to update the filtered measurement 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 all subsets are traversed, the algorithm flow enters box ② and judges each satellite in turn: the test quantity of each satellite is calculated according to formula (23), and the standard deviation of the false alarm rate P is calculated according to formula (24) and the false alarm rate P FA Calculate the corresponding threshold T and compare it with the threshold T. If it is greater than the threshold, the satellite observation value is considered to be a gross error.

[0117] The embodiment of the present invention implements and verifies the method of the present invention based on the PC side, see Figure 2 and Figure 3 .

[0118] Figure 2 The double-difference carrier residuals and their standard deviations of GPS20 satellite (normal satellite) are given in the low-end MEMSIMU (ICM-20602) simulation mode with 3 satellites and 0.5-cycle carrier error. The residual sequence given in the figure is the total residuals of each subset of GPS20 satellite in each epoch. For example, assuming that there are 20 2-satellite subsets in a certain epoch, of which 5 subsets are constructed by GPS20 satellite, then the residuals of 20-5=15 GPS20 satellites can be calculated in this epoch. Figure 2 Results for multiple epochs are presented based on the above. Figure 2 The data shown are collected in an open environment, the carrier observation noise is set to 0.01 cycle, and the position error STD given by the P array is often less than 5 cm. At this time, the theoretical standard deviation of the residual calculated according to formula (20) is about 0.3 cycle, corresponding to the orange curve in the figure. The data simulates a cycle jump of 0.5 cycle, which is reflected in the double-difference carrier residuals with the following rules: 1) When the 2-satellite subset does not contain an abnormal satellite, the residual of the normal satellite in the residual sequence calculated by the subset is <0.5 cycle, and the residual of the abnormal satellite is >0.5 cycle; 2) When the 2-satellite subset contains an abnormal satellite, the residual of the normal satellite and the abnormal satellite in the residual sequence calculated by the subset is >0.5 cycle; 3) The residual spike part is close to the error size.

[0119] Figure 3The figure is a schematic diagram of the fault detection results under the above simulation mode. There are 13 satellites in this epoch, and the 3 simulated abnormal satellites are numbered C14, G25, and G31 respectively. After screening, there are 19 subsets of 2 satellite subsets formed by the arrangement and combination of 13 satellites. The value of each square in the figure represents the ratio of the residual to the standard deviation of the satellite in the subset, and -1 means that the satellite is a sample in the subset. The value after each column of satellite numbers represents the ratio of the test quantity to the threshold. If it is greater than 1, it means that the satellite observation value is judged to be a gross error. It can be seen from the figure that the result is consistent with the simulation.

[0120] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency, which is used to execute a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency in the above method embodiment.

[0121] See also Figure 4 The system includes: a first main module, which is used to use the observation values ​​in the m-satellite subset to perform measurement update to obtain the navigation result of the subset; a second main module, which is used to traverse and calculate the observation value residuals of the satellites outside the subset and the standard deviation of the corresponding residuals one by one according to the navigation result of the subset; a third main module, which is used to perform gross error judgment on each satellite according to the observation value residuals of the satellites outside the subset and the standard deviation of the corresponding residuals after all subsets are traversed, based on the random sampling consistency framework, to form an integrity monitoring result.

[0122] The embodiment of the present invention provides a GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency. Aiming at the problem that the existing GNSS / INS integrated navigation system integrity monitoring method has poor performance in scenarios with insufficient number of visible satellites and multiple gross errors, the present invention adopts a random sampling consistency based GNSS / INS integrated navigation integrity monitoring system to improve the accuracy of the GNSS / INS integrated navigation system integrity monitoring system. Figure 4 Several modules in the proposed algorithm calculate subset residuals and test quantities based on the RANSAC algorithm framework. Fault detection relies on the consistency between observations. The subset model is not affected by multiple gross errors. Compared with the traditional RAIM algorithm, it is suitable for scenarios with less than 4 visible satellites and is not restricted by the assumption that the error distribution is an idealized Gaussian distribution. The subset model is more accurate and can effectively improve the integrity detection performance in scenarios with multiple gross errors or small gross errors.

[0123] It should be noted that the system embodiment provided by the present invention is used to implement the methods in the above method embodiment as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set. The principle is basically the same as the principle of the above system embodiment provided by the present invention. As long as the technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above system embodiment, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above system embodiment to obtain the corresponding system class embodiments, 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, in a GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency provided in an embodiment of the present invention, the third main module is further used to execute the following instructions:

[0125] 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.

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

[0127] 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 smaller than the detection threshold are defined as the 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.

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

[0129] The ratio of the residual and standard deviation of the observation value of the satellite outside the subset is used as a test quantity to distinguish the inner and outer points. While traversing the subset, the number of times each satellite is classified as an outer point is recorded. After all subsets are traversed, the proportion of the number of outer points of each satellite is used to determine whether the satellite is a gross error.

[0130] Based on the contents of the above system embodiments, as a preferred embodiment, a GNSS / INS combined navigation integrity monitoring system based on random sampling consistency provided in an embodiment of the present invention also includes a screening module for determining the sampling number m and screening the subset using the satellite geometric distribution.

[0131] Based on the contents of the above system embodiments, as a preferred embodiment, a GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency provided in an embodiment of the present invention also includes a threshold determination module for determining a detection threshold according to a false alarm rate.

[0132] The embodiment of the present invention also provides a GNSS / INS integrated navigation integrity monitoring device based on random sampling consistency, 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 sampling consistency. Figure 5 As shown, the monitoring device includes: at least one processor, a communication interface, at least one memory, and a communication bus, wherein at least one processor, the communication interface, and at least one memory communicate with each other through the communication bus. At least one processor calls the logic instructions in at least one memory to execute all or part of the steps of the method provided by each of the aforementioned method embodiments.

[0133] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the steps of the GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency.

[0134] When the logic instructions in the at least one memory mentioned above are implemented in the form of a software functional unit and sold or used as an independent product, they are stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly in other words, the part that contributes to the prior art or part of the technical solution is embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for enabling a computer device (a personal computer, a server, or a network device) to perform all or part of the steps of the method described in each method embodiment of the present invention. The aforementioned 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 media for storing program codes.

[0135] The system embodiments described above are merely illustrative, wherein the units described as separate components are or are not physically separated, and the components shown as units are or are not physical units, located in one place, or distributed on multiple network units. Some or all of the modules are selected according to actual conditions to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement without creative effort.

[0136] In summary, the present invention discloses a GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency, including: (1) applying the RANSAC algorithm to GNSS / INS integrated navigation integrity detection, which is applicable to three types of observation values: pseudorange, carrier, and Doppler, and calculating subset residuals and standard deviations based on the RANSAC algorithm framework; (2) determining the number of samples, pre-screening the subsets using satellite geometric distribution, and traversing the subsets to obtain the corresponding residuals and standard deviations of all observation values; (3) including but not limited to calculating the inspection amount using methods such as the optimal subset, the proportion of satellite outliers, and the cost function, and determining the inspection threshold using the false alarm rate.

[0137] The present invention is applicable to scenarios with less than 4 visible satellites, is not restricted by the assumption that the error distribution is an idealized Gaussian distribution, and the subset model is not affected by multiple gross errors. It can effectively improve the integrity detection performance of GNSS / INS integrated navigation in scenarios with multiple gross errors or small gross errors.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency, characterized in that: include: Use the observation values ​​in the m-star subset to perform measurement updates and obtain the navigation results of the subset; According to the navigation results of the subset, traverse and calculate the observation value residuals of satellites outside the subset and the standard deviations of the corresponding residuals one by one; When all subsets are traversed, the random sampling consistency is used as a framework to determine the gross error of each satellite according to the residuals of the observation values ​​of satellites outside the subset and the standard deviation of the corresponding residuals, forming an observation gross error detection result, which provides a good environment for the integrity parameter solution.

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 on 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 on 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 smaller than the detection threshold are defined as the 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 on 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 and standard deviation of the observation value of the satellite outside the subset is used as a test quantity to distinguish the inner and outer points. While traversing the subset, the number of times each satellite is classified as an outer point is recorded. After all subsets are traversed, the proportion of the number of outer points of 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 1, characterized in that: The method further comprises: Determine the sampling number m and use the satellite geometric distribution to screen the subset.

6. The GNSS / INS integrated navigation integrity monitoring method based on random sampling consistency according to claim 1, characterized in that: Calculate the residuals of the observations of satellites outside the subset and the standard deviation of the corresponding residuals, including: calculating with pseudorange, carrier or Doppler as the observation value.

7. 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.

8. A GNSS / INS integrated navigation integrity monitoring system based on random sampling consistency, characterized in that: include: The first main module is used to perform measurement update using the observation values ​​in the m-star subset to obtain the navigation result of the subset; The second main module is used to traverse and calculate the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals one by one according to the navigation results of the subset; The third main module is used to determine the gross error of each satellite based on the observation value residuals of satellites outside the subset and the standard deviation of the corresponding residuals after all subsets are traversed, so as to form an integrity monitoring result.

9. GNSS / INS integrated navigation integrity monitoring equipment based on random sampling consistency, characterized in that: It 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 7.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer 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 7.

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