Navigation system group fault detection and identification method based on cooperative navigation information common mode combination

Abstract

This invention discloses a method for fault detection and identification in a navigation system cluster based on common-mode combination of cooperative navigation information. It utilizes the cooperative system to construct common-mode pseudorange residuals to improve the fault detection sensitivity of the GNSS receiver, and leverages the cooperative characteristics of the cluster system to enhance the efficiency of identifying faulty satellites. By combining and modeling the observations of auxiliary sensors in the cooperative navigation system with the pseudorange observations of the GNSS receiver, the method can detect whether auxiliary sensors have malfunctioned after monitoring the integrity of the GNSS receiver. This method can address various factors that induce pseudorange faults in complex environments, employing decentralized fault handling to improve the fault detection and identification rate of the GNSS receiver in the cooperative navigation system. Furthermore, it detects and identifies faults in auxiliary sensors within the cooperative navigation system, improving the overall integrity of the cooperative navigation system and ensuring its accuracy and robustness.

Description

Technical Field

[0001] This invention relates to the field of measurement and navigation technology, and in particular to a method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information. Background Technology

[0002] Cooperative navigation technology improves overall system navigation accuracy by utilizing relative measurement information obtained during communication and interaction among swarm aircraft for assisted positioning. Global Navigation Satellite System (GNSS) technology is the most widely used. For cooperative navigation systems, while achieving relatively high positioning accuracy using GNSS, relative measurement information from auxiliary sensors such as data links, inertial navigation, and visual navigation is introduced based on the specific application scenario to improve the positioning performance and reliability of the entire navigation system. However, in addition to positioning accuracy, the integrity of the cooperative navigation system should also be considered to ensure its accuracy and robustness.

[0003] For example, swarm aircraft may experience sensor malfunctions during missions. If the navigation system fails to issue a timely warning after such a malfunction, it can lead to serious consequences. In navigation systems, receiver autonomous integrity monitoring (RAIM) is a technology built into GNSS receivers used to monitor pseudorange residuals to determine if a satellite malfunction has occurred. While it offers advantages such as rapid warning capabilities and independence from external equipment, its fault detection sensitivity drops significantly in complex environments such as limited available satellites, multipath signals, or non-line-of-sight (NLOS) signals. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies mentioned in the background art by providing a method for fault detection and identification of navigation system groups based on the common mode combination of cooperative navigation information.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for fault detection and identification of navigation system groups using collaborative navigation information sharing includes the following steps:

[0007] Step 1): Each aircraft in the cluster system uses a GNSS receiver to acquire satellite pseudorange observations and uses a data link as an auxiliary sensor to acquire relative distance information between aircraft. An observation equation is established based on the satellite pseudorange observations and relative distance observations.

[0008] The swarm consists of M aircraft, one of which is the target aircraft v, where v ∈ {1, 2, ..., M}, and the rest are auxiliary aircraft k, where k ∈ {1, 2, ..., M}. The number of visible satellites for the target aircraft v and the auxiliary aircraft k are n, respectively. v and n k;

[0009] Step 1.1) Calculate the satellite pseudorange observations of each spacecraft in the cluster system. The pseudorange observation equation for auxiliary spacecraft k is:

[0010]

[0011] in, To assist spacecraft k in its operation, among all currently visible satellites, up to the [number]th [satellite name]... Pseudorange measurements of 1 visible satellite, j∈{1,2,…,n} k}, To assist the K-plane in reaching the satellite The true value of the distance, where η is the speed of light, and δt is the distance. u Let ε be the clock error of the GNSS equipment onboard the aircraft k. ρ This includes pseudorange noise, which includes satellite clock errors, ionospheric and tropospheric delays, and errors caused by signal multipath and non-line-of-sight.

[0012] Step 1.2), the relative distance observation equation between the target aircraft v and the auxiliary aircraft k is calculated as follows:

[0013]

[0014] in, d represents the relative distance measurement between the target aircraft v and the auxiliary aircraft k. k Let δt be the true relative distance between the target aircraft v and the auxiliary aircraft k. r For the clock bias of the data link system on the target aircraft v, δt s To assist the clock bias of the onboard data link system of aircraft k, ε d Measurement noise for data link systems

[0015] Step 2) Within the cluster network, each aircraft interacts with each other, shares its own observations, and each aircraft unit linearizes its observation equations and calculates the corresponding pseudorange residuals and relative range residuals.

[0016] Step 3) Each spacecraft constructs its own observation matrix based on the visible satellites and distance information to other spacecraft, and establishes a combined model of pseudorange residual and relative distance residual. The measurement noise of the observations is normalized, and the state estimation solution of the target spacecraft is calculated.

[0017] Step 4): Each spacecraft broadcasts the number and quantity information of its visible satellites in the cluster network; the target spacecraft compares the number of its own visible satellites with the numbers of all visible satellites of each other spacecraft, and records the visible satellites with the same number; the visible satellites with the same number among the spacecraft are designated as the common-mode satellites of the spacecraft.

[0018] Step 5) The target spacecraft selects all auxiliary spacecraft in the cluster system that share the same mode with it, calculates the pseudorange residual vector of each spacecraft based on the observation matrix of each spacecraft, and constructs the common mode pseudorange residual statistical detection quantity.

[0019] Step 6) Optimize the common-mode pseudorange residual statistical detection quantity. Before the target aircraft performs fault detection, calculate in the navigation system which auxiliary aircraft to select to build common-mode detection statistics to make the current fault detection performance of the navigation system the best.

[0020] Step 7) Calculate the fault detection threshold of the target aircraft and compare it with the statistical detection value of its common-mode pseudorange residual to determine whether a pseudorange measurement fault has occurred.

[0021] Step 8): If a fault exists, the target aircraft will identify and eliminate the fault. Once the system is free of pseudorange faults, it will further determine whether the data link used in the cooperative navigation system has failed.

[0022] Step 9): The target aircraft uses its combined model to construct its cooperative detection statistics, calculates its cooperative detection threshold, compares its cooperative statistical detection quantity with the size of the cooperative detection threshold, and determines whether the relative distance information of the data link has failed.

[0023] Step 10): If a data link measurement fault exists in the system, fault identification and isolation procedures will be performed.

[0024] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 2) are as follows:

[0025] Step 2.1), calculate the distance from the auxiliary spacecraft k to the currently visible satellite. The geometric distance is:

[0026]

[0027] in, Visible satellites for auxiliary spacecraft k The position, in the three-dimensional coordinates of the Earth-Centered, Earth Fixed, ECEF coordinate system, is: p k To help determine the position of spacecraft k, its three-dimensional position coordinates in the Earth's fixed coordinate system are (x... k ,y k ,z k );

[0028] Step 2.2), move the auxiliary spacecraft k to its visible satellite. The geometric distance is expanded using a Taylor series and then linearized, resulting in the following expression:

[0029]

[0030]

[0031] Where, δx k ,δy k ,δz k These represent the position errors of the auxiliary aircraft k in the X, Y, and Z directions in the ECEF coordinate system, respectively. These are the auxiliary aircraft K and the satellite, respectively. The direction cosines in the X, Y, and Z directions, Determine the direction cosine of the position error of a visible star;

[0032] Step 2.3), calculate the distance from the auxiliary spacecraft k to its visible satellite. The difference between the calculated and measured distance values ​​yields the pseudorange residual value of the auxiliary aircraft k:

[0033]

[0034] Step 2.4), calculate the geometric distance between the target aircraft v and the auxiliary aircraft k as follows:

[0035]

[0036] Where, p v Let v be the position of the target aircraft. Its three-dimensional position coordinates in the ECEF coordinate system are (x...). v ,y v ,z v ), p k To determine the position of the auxiliary aircraft k, its three-dimensional position coordinates in the ECEF coordinate system are (x... k ,y k ,z k );

[0037] Step 2.5): Perform a Taylor series expansion and linearization on the geometric distance between the target aircraft v and the auxiliary aircraft k. The transformed expression is:

[0038]

[0039]

[0040] Where δx, δy, and δz represent the position errors of the target aircraft v in the X, Y, and Z directions in the ECEF coordinate system, respectively. Let X and Z be the direction cosines of the auxiliary aircraft k and the target aircraft v, respectively, in the X, Y, and Z directions. This represents the calculation of the direction cosine over the position error of the auxiliary aircraft k.

[0041] Step 2.6), calculate the relative distance residual from target aircraft v to aircraft k as follows:

[0042]

[0043] Step 2.7), based on the pseudorange residual of the auxiliary aircraft k, the pseudorange residual of the target aircraft v in the swarm system is obtained as follows:

[0044]

[0045] in, For the target spacecraft v, among all currently visible satellites, up to the [number]th Pseudorange measurements of 1 visible satellite, j∈{1,2,…,n} v}, For the target spacecraft v to reach its visible satellite The distance calculation value, The target spacecraft v and the visible satellite are respectively. Direction cosines in the X, Y, and Z directions.

[0046] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, step 3) is as follows:

[0047] Step 3.1): The target aircraft v and the auxiliary aircraft k construct observation matrices based on their own pseudorange observation information and relative distance information, respectively. and The observation matrix H of the target aircraft v v The observation matrix H of the satellite v,g and the observation matrix H of other auxiliary aircraft v,d The composition is as follows:

[0048]

[0049]

[0050] Step 3.2) The target aircraft v will be modeled by combining its relative distance residual to the auxiliary aircraft k and the pseudorange residual of the target aircraft v as follows:

[0051] y v =H v ·x v +ε v

[0052] in, For the distance residual observation of the target aircraft v, x v =[δx,δy,δz,δt] u ] T For the state variables of the target aircraft, Measurement noise of the target aircraft's onboard sensors;

[0053] Step 3.3): Select a weighting matrix W to normalize the variance of the pseudorange measurement noise and the variance of the measurement noise. The specific format of the weighting matrix W is as follows:

[0054]

[0055] in, The variance of pseudorange observation noise. The variance of the ranging noise;

[0056] Step 3.4): Based on the weighted least squares principle, calculate the estimation error vector. The weighted least squares state estimate of the target aircraft v when the sum of squares takes its minimum value is:

[0057]

[0058] Among them, W v This is the weighted matrix corresponding to the target aircraft.

[0059] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 4) are as follows:

[0060] Step 4.1): Each spacecraft detects all its own visible satellite information and interacts within the cluster network, enabling each spacecraft to observe the visible satellite IDs and quantities of other spacecraft in real time; the set of visible satellite IDs for target spacecraft v is... The set of visible satellite numbers for auxiliary spacecraft k is

[0061] Step 4.2): The target spacecraft compares its own set of visible satellite IDs with the sets of visible satellite IDs of other auxiliary spacecraft. If there are identical visible satellite IDs, i.e., common-mode satellites of target spacecraft v and auxiliary spacecraft k, the satellite ID is stored in the common-mode satellite set of target spacecraft v and auxiliary spacecraft k. In the formula, The number of common-mode satellites for the target spacecraft v and the auxiliary spacecraft k.

[0062] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 5) are as follows:

[0063] Step 5.1), extract the state estimation solution for the pseudorange observation of the target aircraft v from step 3.3) as follows:

[0064]

[0065] in For the pseudorange residual observation of the target aircraft v, The pseudorange observation weighting matrix for the target aircraft v;

[0066] Step 5.2), calculate the pseudorange residual vectors of the target spacecraft v and the auxiliary spacecraft k with which it shares a common-mode satellite, respectively:

[0067]

[0068]

[0069] in, The pseudorange measurement noises for the target vehicle v and the auxiliary vehicle k are respectively; W k,g For the pseudorange weighting matrix of the auxiliary aircraft k; Let v be the pseudorange residual sensitivity matrix of the target aircraft v;

[0070] Step 5.3): Within the cluster network, the target aircraft v simultaneously acquires pseudorange measurements from the other auxiliary aircraft k that share the same pseudorange mode. The number of auxiliary aircraft k is G (G≤M). For any given moment, the target aircraft, in addition to its own pseudorange residual vector r, acquires pseudorange measurements from the other auxiliary aircraft k. v In addition, G common-mode pseudorange residual vectors can be obtained, denoted as Therefore, the common-mode pseudorange residual detection statistic constructed in a collaborative environment is:

[0071]

[0072] in, It is the pseudorange weighted matrix for all auxiliary spacecraft k that share a common mode with the target spacecraft.

[0073] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, step 6) includes the following specific steps:

[0074] Step 6.1): In the current navigation system, each spacecraft constructs the common-mode pseudorange residual vector based on the common-mode satellite set in step 4.2). The common-mode satellite ratio of each auxiliary spacecraft k relative to the target spacecraft v in the computing cluster system is:

[0075]

[0076] Where, β k To determine the proportion of common-mode satellites between auxiliary spacecraft k and target spacecraft v;

[0077] Step 6.2): ​​Based on the calculated common-mode satellite ratio, the auxiliary spacecraft are matched and screened. Each auxiliary spacecraft k is sorted in descending order of its common-mode satellite ratio relative to the target spacecraft v. When calculating the common-mode detection statistic, the auxiliary spacecraft with the highest common-mode satellite ratio among the current spacecraft is included to assist in constructing the detection statistic. And calculate the corresponding detection threshold as follows:

[0078]

[0079]

[0080] Where E (E≤G) represents the total number of auxiliary aircraft that have constructed the detection statistics in the current system, and r is the pseudorange residual vector corresponding to the target aircraft and the auxiliary aircraft selected at this time. c , P is the pseudorange weighting matrix of the auxiliary aircraft involved in constructing the common mode detection statistics in the auxiliary aircraft matching scheme. FA The false alarm rate is given by the system. The number of visible satellites for the selected auxiliary spacecraft. For χ 2 The inverse cumulative distribution function of the distribution;

[0081] Step 6.3), according to and The threshold ratio (STR) for target aircraft fault detection after screening is calculated as follows:

[0082]

[0083] Step 6.4) Compare the current limit ratio STR with the previous limit ratio. If the current limit ratio STR is greater than or equal to the previous limit ratio, proceed to step 6.2). Otherwise, take the auxiliary aircraft selected by the previous calculated limit ratio and obtain the maximum value of the limit ratio, which represents the best fault detection performance.

[0084] Step 6.5) Select the auxiliary aircraft matching scheme corresponding to the maximum control ratio, and use it as the screened common-mode auxiliary aircraft to assist the target aircraft v in performing fault detection according to the method in step 7).

[0085] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 7) are as follows:

[0086] Step 7.1): Calculate the fault detection threshold for each aircraft based on the preset false alarm rate. The formula for calculating the fault detection threshold is as follows:

[0087]

[0088] in, Let v be the probability that the target aircraft does not have a pseudorange fault. It has n degrees of freedom v The probability density function of the chi-square distribution with a value of -4, P FA The false alarm rate given by the system;

[0089] Step 7.2) compare the common-mode pseudorange residual detection statistics. and fault detection threshold The size of the satellite is used to determine whether there is a satellite malfunction. If this occurs, it indicates that the system has detected a satellite malfunction.

[0090] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 8) are as follows:

[0091] Step 8.1): If a fault is detected in step 7.2), the fault is identified. The target aircraft v excludes its own pseudorange faults. Using the Balda data detection method, the constructed fault identification statistics are as follows:

[0092]

[0093] Where, r v,l Let σ be the l-th element of the pseudorange residual vector of the target aircraft v. ρ The standard deviation of pseudorange observation noise. For the target aircraft v residual sensitive matrix S v,g The element in the l-th row and l-th column;

[0094] Step 8.2) Calculate the fault identification threshold of the target aircraft v. The calculation formula is as follows:

[0095]

[0096] Among them, P r (d v,l >T v,d T represents the probability that the identification statistic of the target aircraft v is greater than the fault detection threshold. v,dThe fault identification threshold for the target aircraft v. The probability density function is the standard normal distribution.

[0097] Step 8.3): Compare the pseudo-distance fault identification statistics calculated in step 8.1) with the fault identification threshold calculated in step 8.2), perform pseudo-distance fault identification, and isolate the identified faults.

[0098] Step 8.4): After the pseudorange fault is detected, identified and eliminated, the combined model in step 3.1) is used to detect and identify the ranging fault of the data link according to step 9).

[0099] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 9) are as follows:

[0100] Step 9.1): Based on the combined model of target aircraft v, calculate the cooperative residual vector of target aircraft v as follows:

[0101] ω v =(IH v (H v T W v H v ) -1 H v T W v )·ε v

[0102] Let matrix S v =IH v (H v T W v H v ) -1 H v T W v For the cooperative residual sensitive matrix;

[0103] Step 9.2), calculate the cooperative residual weighted sum of squares (SSE) of the target aircraft v according to the following formula. v :

[0104]

[0105] Step 9.3) Calculate the posterior weight error of the weighted sum of squares of the cooperative residuals. As a statistical measure of collaborative detection of target aircraft:

[0106]

[0107] Step 9.4): Calculate the cooperative fault detection threshold for each aircraft based on the false alarm rate given by the system. The formula for calculating the fault detection threshold is as follows:

[0108]

[0109] Among them, P r (SSE v <T v T represents the probability that the relative distance information between the target aircraft v and the data link is fault-free. v The collaborative fault detection threshold for the target aircraft v;

[0110] Step 9.5): To facilitate a unified comparison, calculate the fault detection limit σ corresponding to the cooperative fault detection statistic of the target aircraft in Step 9.3). v,T :

[0111]

[0112] Step 9.6): Compare the cooperative fault detection statistic calculated in Step 9.3) with the fault detection limit calculated in Step 9.5) to determine whether the target aircraft's data link relative ranging information has failed. This indicates a fault in the target aircraft's data link ranging data; otherwise, there is no fault.

[0113] As a further optimization of the navigation system group fault detection and identification method of the collaborative navigation information common mode combination of the present invention, the specific steps of step 10) are as follows:

[0114] Step 10.1): Based on the judgment result in Step 9), if a fault occurs, the fault is identified and isolated. The fault identification statistic is constructed using the least squares collaborative residual vector:

[0115]

[0116] Where, ω v,l This represents the l-th element of the cooperative residual vector of the target aircraft v. The target aircraft v weighted matrix W v The element in the l-th row and l-th column, The target aircraft v cooperative residual sensitive matrix S represents the target aircraft v cooperative residual sensitive matrix S. v The element in the l-th row and l-th column;

[0117] Step 10.2): In the entire cooperative navigation system, since the number of auxiliary aircraft is M-1 and the number of visible satellites of the target aircraft is n, we obtain M+n. v -1 identification statistic, based on a given false alarm rate P. FA Then the false alarm rate for each identification statistic is P.FA / (M+n v -1), the formula for calculating the limit value for fault identification of the data link is:

[0118]

[0119] Among them, P r (d v,l >Q v,d Q represents the probability that the identification statistic of the target aircraft v is greater than the fault detection threshold. v,d The threshold value for data link fault identification of target aircraft v. The probability density function is the standard normal distribution.

[0120] Step 10.3): Based on the detection threshold calculated in step 10.2), compare each identification statistic q. v,l With recognition threshold Q v,d The size of q determines which auxiliary aircraft's ranging data from the target aircraft v is faulty: if q v,l >Q v,d If the target aircraft v fails to reach the auxiliary aircraft lM-1, the ranging data from the target aircraft v to the auxiliary aircraft lM-1 is faulty; therefore, the navigation system will further isolate the faulty data link ranging data. Otherwise, it indicates that there is no data link ranging fault.

[0121] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0122] 1. Compared with traditional satellite fault detection, the method of the present invention can greatly improve fault detection sensitivity by constructing common-mode fault detection statistics in a cluster environment. Furthermore, by utilizing the cooperative characteristics of clustered aircraft, fault identification information of all aircraft can be shared, thereby improving the fault identification rate and accuracy.

[0123] 2. Regarding the various factors that induce pseudorange faults, the method of this invention can not only detect and identify common pseudorange faults caused by factors such as satellite malfunctions, tropospheric delay, and ionospheric delay, resulting in abnormal pseudorange measurements of a certain visible satellite by all spacecraft within the cluster network, but also detect and identify specific pseudorange faults caused by factors such as signal multipath or non-line-of-sight (NLOS), resulting in abnormal satellite pseudorange measurements by some spacecraft. Regardless of the type of fault, each spacecraft performs decentralized fault handling without interfering with each other.

[0124] 3. Compared with traditional fault detection techniques, the method of this invention can not only detect satellite pseudorange faults, but also monitor the integrity of any auxiliary sensor used in the navigation system, perform fault detection and identification, and ensure the reliability and accuracy of the entire navigation system.

[0125] 4. The present invention proposes a method to optimize fault detection performance based on common mode. For cluster system integrity monitoring, before each aircraft performs fault detection, the navigation system will first calculate which auxiliary aircraft to build common mode detection statistics with, so as to make the current fault detection performance of the navigation system optimal, thereby improving the fault detection rate of the navigation system. Attached Figure Description

[0126] Figure 1 This is a schematic diagram illustrating the principle and flow of the method of the present invention;

[0127] Figure 2 This is a schematic diagram of a swarm aircraft according to the method of the present invention;

[0128] Figure 3 This is a schematic diagram showing the number of visible satellites for each spacecraft in the method of this invention;

[0129] Figure 4 To compare the fault detection rates using the method of this invention with two methods not using the method of this invention;

[0130] Figure 5 (a) is a faulty satellite identification image that was not generated using the method of the present invention;

[0131] Figure 5 (b) A faulty satellite identification image using the method of the present invention;

[0132] Figure 6 (a) Pseudorange observation error curves for each aircraft using the common pseudorange fault in the method of the present invention;

[0133] Figure 6 (b) Pseudorange observation error curves for a portion of aircraft employing a specific pseudorange fault in the method of the present invention;

[0134] Figure 6 (c) Detection images of different types of pseudorange faults using the method of the present invention;

[0135] Figure 6 (d) is a comparison image of the fault detection rate of different types of pseudorange faults using the method of the present invention;

[0136] Figure 7 (a) is a comparison chart of visible satellite numbers before and after the introduction of non-common-mode satellites in the method of the present invention;

[0137] Figure 7 (b) is a comparison chart of the visible satellite numbers before and after the introduction of some common-mode satellites in the method of the present invention;

[0138] Figure 7 (c) A comparison of system fault detection performance after introducing common-mode satellites under different conditions using the method of the present invention;

[0139] Figure 7 (d) shows the performance changes of fault detection in the cluster system using the method of the present invention;

[0140] Figure 8 (a) A fault detection image of an auxiliary sensor in a navigation system using the method of the present invention;

[0141] Figure 8 (b) A fault identification image of an auxiliary sensor in a navigation system using the method of the present invention. Detailed Implementation

[0142] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings:

[0143] This invention can be implemented in many different forms and should not be considered limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully express the scope of the invention to those skilled in the art. In the drawings, components are enlarged for clarity.

[0144] like Figure 1 As shown, this invention discloses a method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information, comprising the following steps:

[0145] Step 1): Each aircraft in the cluster system uses a GNSS receiver to acquire satellite pseudorange observations and uses a data link as an auxiliary sensor to acquire relative distance information between aircraft. An observation equation is established based on the satellite pseudorange observations and relative distance observations.

[0146] The swarm consists of M aircraft, one of which is the target aircraft v, where v ∈ {1, 2, ..., M}, and the rest are auxiliary aircraft k, where k ∈ {1, 2, ..., M}. The number of visible satellites for the target aircraft v and the auxiliary aircraft k are n, respectively. v and n k ;

[0147] Step 1.1) Calculate the satellite pseudorange observations of each spacecraft in the cluster system. The pseudorange observation equation for auxiliary spacecraft k is:

[0148]

[0149] in, To assist spacecraft k in its operation, among all currently visible satellites, up to the [number]th [satellite name]... Pseudorange measurements of 1 visible satellite, j∈{1,2,…,n} k}, To assist the K-plane in reaching the satellite The true value of the distance, where η is the speed of light, and δt is the distance.u Let ε be the clock error of the GNSS equipment onboard the aircraft k. ρ This includes pseudorange noise, which includes satellite clock errors, ionospheric and tropospheric delays, and errors caused by signal multipath and non-line-of-sight.

[0150] Step 1.2), the relative distance observation equation between the target aircraft v and the auxiliary aircraft k is calculated as follows:

[0151]

[0152] in, d represents the relative distance measurement between the target aircraft v and the auxiliary aircraft k. k Let δt be the true relative distance between the target aircraft v and the auxiliary aircraft k. r For the clock bias of the data link system on the target aircraft v, δt s To assist the clock bias of the onboard data link system of aircraft k, ε d Measurement noise for data link systems

[0153] Step 2) Within the cluster network, each aircraft interacts with each other, shares its own observations, and each aircraft unit linearizes its observation equations and calculates the corresponding pseudorange residuals and relative range residuals.

[0154] Step 2.1), calculate the distance from the auxiliary spacecraft k to the currently visible satellite. The geometric distance is:

[0155]

[0156] in, Visible satellites for auxiliary spacecraft k The position, in the three-dimensional coordinates of the Earth-Centered, Earth Fixed, ECEF coordinate system, is: p k To help determine the position of spacecraft k, its three-dimensional position coordinates in the Earth's fixed coordinate system are (x... k ,y k ,z k );

[0157] Step 2.2), move the auxiliary spacecraft k to its visible satellite. The geometric distance is expanded using a Taylor series and then linearized, resulting in the following expression:

[0158]

[0159]

[0160] Where, δx k ,δy k ,δz k These represent the position errors of the auxiliary aircraft k in the X, Y, and Z directions in the ECEF coordinate system, respectively. These are the auxiliary aircraft K and the satellite, respectively. The direction cosines in the X, Y, and Z directions, Determine the direction cosine of the position error of a visible star;

[0161] Step 2.3), calculate the distance from the auxiliary spacecraft k to its visible satellite. The difference between the calculated and measured distance values ​​yields the pseudorange residual value of the auxiliary aircraft k:

[0162]

[0163] Step 2.4), calculate the geometric distance between the target aircraft v and the auxiliary aircraft k as follows:

[0164]

[0165] Where, p v Let v be the position of the target aircraft. Its three-dimensional position coordinates in the ECEF coordinate system are (x...). v ,y v ,z v ), p k To determine the position of the auxiliary aircraft k, its three-dimensional position coordinates in the ECEF coordinate system are (x... k ,y k ,z k );

[0166] Step 2.5): Perform a Taylor series expansion and linearization on the geometric distance between the target aircraft v and the auxiliary aircraft k. The transformed expression is:

[0167]

[0168]

[0169] Where δx, δy, and δz represent the position errors of the target aircraft v in the X, Y, and Z directions in the ECEF coordinate system, respectively. Let X and Z be the direction cosines of the auxiliary aircraft k and the target aircraft v, respectively, in the X, Y, and Z directions. This represents the calculation of the direction cosine over the position error of the auxiliary aircraft k.

[0170] Step 2.6), calculate the relative distance residual from target aircraft v to aircraft k as follows:

[0171]

[0172] Step 2.7), based on the pseudorange residual of the auxiliary aircraft k, the pseudorange residual of the target aircraft v in the swarm system is obtained as follows:

[0173]

[0174] in, For the target spacecraft v, among all currently visible satellites, up to the [number]th Pseudorange measurements of 1 visible satellite, j∈{1,2,…,n} v}, For the target spacecraft v to reach its visible satellite The distance calculation value, The target spacecraft v and the visible satellite are respectively. The direction cosine in the X, Y, and Z directions;

[0175] Step 3) Each spacecraft constructs its own observation matrix based on the visible satellites and distance information to other spacecraft, and establishes a combined model of pseudorange residual and relative distance residual. The measurement noise of the observations is normalized, and the state estimation solution of the target spacecraft is calculated.

[0176] Step 3.1): The target aircraft v and the auxiliary aircraft k construct observation matrices based on their own pseudorange observation information and relative distance information, respectively. and The observation matrix H of the target aircraft v v The observation matrix H of the satellite v,g and the observation matrix H of other auxiliary aircraft v,d The composition is as follows:

[0177]

[0178]

[0179] Step 3.2) The target aircraft v will be modeled by combining its relative distance residual to the auxiliary aircraft k and the pseudorange residual of the target aircraft v as follows:

[0180] y v =H v ·x v +ε v

[0181] in, For the distance residual observation of the target aircraft v, x v =[δx,δy,δz,δt] u ] T For the state variables of the target aircraft, Measurement noise of the target aircraft's onboard sensors;

[0182] Step 3.3): Select a weighting matrix W to normalize the variance of the pseudorange measurement noise and the variance of the measurement noise. The specific format of the weighting matrix W is as follows:

[0183]

[0184] in, The variance of pseudorange observation noise. The variance of the ranging noise;

[0185] Step 3.4): Based on the weighted least squares principle, calculate the estimation error vector. The weighted least squares state estimate of the target aircraft v when the sum of squares takes its minimum value is:

[0186]

[0187] Among them, W v This is the weighted matrix corresponding to the target aircraft;

[0188] Step 4): Each spacecraft broadcasts the number and quantity information of its visible satellites in the cluster network; the target spacecraft compares the number of its own visible satellites with the numbers of all visible satellites of each other spacecraft, and records the visible satellites with the same number; the visible satellites with the same number among the spacecraft are designated as the common-mode satellites of the spacecraft.

[0189] Step 4.1): Each spacecraft detects all its own visible satellite information and interacts within the cluster network, enabling each spacecraft to observe the visible satellite IDs and quantities of other spacecraft in real time; the set of visible satellite IDs for target spacecraft v is... The set of visible satellite numbers for auxiliary spacecraft k is

[0190] Step 4.2): The target spacecraft compares its own set of visible satellite IDs with the sets of visible satellite IDs of other auxiliary spacecraft. If there are identical visible satellite IDs, i.e., common-mode satellites of target spacecraft v and auxiliary spacecraft k, the satellite ID is stored in the common-mode satellite set of target spacecraft v and auxiliary spacecraft k. In the formula, The number of common-mode satellites for the target spacecraft v and the auxiliary spacecraft k;

[0191] Step 5) The target spacecraft selects all auxiliary spacecraft in the cluster system that share the same mode with it, calculates the pseudorange residual vector of each spacecraft based on the observation matrix of each spacecraft, and constructs the common mode pseudorange residual statistical detection quantity.

[0192] Step 5.1), extract the state estimation solution for the pseudorange observation of the target aircraft v from step 3.3) as follows:

[0193]

[0194] in For the pseudorange residual observation of the target aircraft v, The pseudorange observation weighting matrix for the target aircraft v;

[0195] Step 5.2), calculate the pseudorange residual vectors of the target spacecraft v and the auxiliary spacecraft k with which it shares a common-mode satellite, respectively:

[0196]

[0197]

[0198] in, The pseudorange measurement noises for the target vehicle v and the auxiliary vehicle k are respectively; W k,g For the pseudorange weighting matrix of the auxiliary aircraft k; Let v be the pseudorange residual sensitivity matrix of the target aircraft v;

[0199] Step 5.3): Within the cluster network, the target aircraft v simultaneously acquires pseudorange measurements from the other auxiliary aircraft k that share the same pseudorange mode. The number of auxiliary aircraft k is G (G≤M). For any given moment, the target aircraft, in addition to its own pseudorange residual vector r, acquires pseudorange measurements from the other auxiliary aircraft k. v In addition, G common-mode pseudorange residual vectors can be obtained, denoted as Therefore, the common-mode pseudorange residual detection statistic constructed in a collaborative environment is:

[0200]

[0201] in, The pseudorange weighted matrix is ​​the pseudorange weighted matrix for all auxiliary spacecraft k that share a common mode with the target spacecraft;

[0202] Step 6) Optimize the common-mode pseudorange residual statistical detection quantity. Before the target aircraft performs fault detection, calculate in the navigation system which auxiliary aircraft to select to build common-mode detection statistics to make the current fault detection performance of the navigation system the best.

[0203] Step 6.1): In the current navigation system, each spacecraft constructs the common-mode pseudorange residual vector based on the common-mode satellite set in step 4.2). The common-mode satellite ratio of each auxiliary spacecraft k relative to the target spacecraft v in the computing cluster system is:

[0204]

[0205] Where, β k To determine the proportion of common-mode satellites between auxiliary spacecraft k and target spacecraft v;

[0206] Step 6.2): ​​Based on the calculated common-mode satellite ratio, the auxiliary spacecraft are matched and screened. Each auxiliary spacecraft k is sorted in descending order of its common-mode satellite ratio relative to the target spacecraft v. When calculating the common-mode detection statistic, the auxiliary spacecraft with the highest common-mode satellite ratio among the current spacecraft is included to assist in constructing the detection statistic. And calculate the corresponding detection threshold as follows:

[0207]

[0208]

[0209] Where E (E≤G) represents the total number of auxiliary aircraft that have constructed the detection statistics in the current system, and r is the pseudorange residual vector corresponding to the target aircraft and the auxiliary aircraft selected at this time. c , P is the pseudorange weighting matrix of the auxiliary aircraft involved in constructing the common mode detection statistics in the auxiliary aircraft matching scheme. FA The false alarm rate is given by the system. The number of visible satellites for the selected auxiliary spacecraft. For χ 2 The inverse cumulative distribution function of the distribution;

[0210] Step 6.3), according to and The threshold ratio (STR) for target aircraft fault detection after screening is calculated as follows:

[0211]

[0212] Step 6.4) Compare the current limit ratio STR with the previous limit ratio. If the current limit ratio STR is greater than or equal to the previous limit ratio, proceed to step 6.2). Otherwise, take the auxiliary aircraft selected by the previous calculated limit ratio and obtain the maximum value of the limit ratio, which represents the best fault detection performance.

[0213] Step 6.5) Select the auxiliary aircraft matching scheme corresponding to the maximum control ratio, and use it as the screened common-mode auxiliary aircraft to assist the target aircraft v in performing fault detection according to the method in step 7).

[0214] Step 7) Calculate the fault detection threshold of the target aircraft and compare it with the statistical detection value of its common-mode pseudorange residual to determine whether a pseudorange measurement fault has occurred.

[0215] Step 7.1): Calculate the fault detection threshold for each aircraft based on the preset false alarm rate. The formula for calculating the fault detection threshold is as follows:

[0216]

[0217] in, Let v be the probability that the target aircraft does not have a pseudorange fault. It has n degrees of freedom v The probability density function of the chi-square distribution with a value of -4, P FA The false alarm rate given by the system;

[0218] Step 7.2) compare the common-mode pseudorange residual detection statistics. and fault detection threshold The size of the satellite is used to determine whether there is a satellite malfunction. If this occurs, it indicates that the system has detected a satellite malfunction;

[0219] Step 8): If a fault exists, the target aircraft will identify and eliminate the fault. Once the system is free of pseudorange faults, it will further determine whether the data link used in the cooperative navigation system has failed.

[0220] Step 8.1): If a fault is detected in step 7.2), the fault is identified. The target aircraft v excludes its own pseudorange faults. Using the Balda data detection method, the constructed fault identification statistics are as follows:

[0221]

[0222] Where, r v,l Let σ be the l-th element of the pseudorange residual vector of the target aircraft v. ρ The standard deviation of pseudorange observation noise. For the target aircraft v residual sensitive matrix S v,g The element in the l-th row and l-th column;

[0223] Step 8.2) Calculate the fault identification threshold of the target aircraft v. The calculation formula is as follows:

[0224]

[0225] Among them, P r (d v,l >T v,d T represents the probability that the identification statistic of the target aircraft v is greater than the fault detection threshold. v,dThe fault identification threshold for the target aircraft v. The probability density function is the standard normal distribution.

[0226] Step 8.3): Compare the pseudo-distance fault identification statistics calculated in step 8.1) with the fault identification threshold calculated in step 8.2), perform pseudo-distance fault identification, and isolate the identified faults.

[0227] Step 8.4): After the pseudorange fault is detected, identified and eliminated, the combined model in step 3.1) is used to detect and identify the ranging fault of the data link according to step 9).

[0228] Step 9): The target aircraft uses its combined model to construct its cooperative detection statistics, calculates its cooperative detection threshold, compares its cooperative statistical detection quantity with the size of the cooperative detection threshold, and determines whether the relative distance information of the data link has failed.

[0229] Step 9.1): Based on the combined model of target aircraft v, calculate the cooperative residual vector of target aircraft v as follows:

[0230] ω v =(IH v (H v T W v H v ) -1 H v T W v )·ε v

[0231] Let matrix S v =IH v (H v T W v H v ) -1 H v T W v For the cooperative residual sensitive matrix;

[0232] Step 9.2), calculate the cooperative residual weighted sum of squares (SSE) of the target aircraft v according to the following formula. v :

[0233]

[0234] Step 9.3) Calculate the posterior weight error of the weighted sum of squares of the cooperative residuals. As a statistical measure of collaborative detection of target aircraft:

[0235]

[0236] Step 9.4): Calculate the cooperative fault detection threshold for each aircraft based on the false alarm rate given by the system. The formula for calculating the fault detection threshold is as follows:

[0237]

[0238] Among them, P r (SSE v <T v T represents the probability that the relative distance information between the target aircraft v and the data link is fault-free. v The collaborative fault detection threshold for the target aircraft v;

[0239] Step 9.5): To facilitate a unified comparison, calculate the fault detection limit σ corresponding to the cooperative fault detection statistic of the target aircraft in Step 9.3). v,T :

[0240]

[0241] Step 9.6): Compare the cooperative fault detection statistic calculated in Step 9.3) with the fault detection limit calculated in Step 9.5) to determine whether the target aircraft's data link relative ranging information has failed. This indicates a fault in the target aircraft's data link ranging data; otherwise, there is no fault.

[0242] Step 10): If a data link measurement fault exists in the system, fault identification and isolation procedures will be performed.

[0243] Step 10.1): Based on the judgment result in Step 9), if a fault occurs, the fault is identified and isolated. The fault identification statistic is constructed using the least squares collaborative residual vector:

[0244]

[0245] Where, ω v,l This represents the l-th element of the cooperative residual vector of the target aircraft v. The target aircraft v weighted matrix W v The element in the l-th row and l-th column, The target aircraft v cooperative residual sensitive matrix S represents the target aircraft v cooperative residual sensitive matrix S. v The element in the l-th row and l-th column;

[0246] Step 10.2): In the entire cooperative navigation system, since the number of auxiliary aircraft is M-1 and the number of visible satellites of the target aircraft is n, we obtain M+n. v -1 identification statistic, based on a given false alarm rate P. FA Then the false alarm rate for each identification statistic is P.FA / (M+n v -1), the formula for calculating the limit value for fault identification of the data link is:

[0247]

[0248] Among them, P r (d v,l >Q v,d Q represents the probability that the identification statistic of the target aircraft v is greater than the fault detection threshold. v,d The threshold value for data link fault identification of target aircraft v. The probability density function is the standard normal distribution.

[0249] Step 10.3): Based on the detection threshold calculated in step 10.2), compare each identification statistic q. v,l With recognition threshold Q v,d The size of q determines which auxiliary aircraft's ranging data from the target aircraft v is faulty: if q v,l >Q v,d If the target aircraft v fails to reach the auxiliary aircraft lM-1, the ranging data from the target aircraft v to the auxiliary aircraft lM-1 is faulty; therefore, the navigation system will further isolate the faulty data link ranging data. Otherwise, it indicates that there is no data link ranging fault.

[0250] To verify the effectiveness of the proposed method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information, digital simulation analysis was conducted. Figure 2 This is a schematic diagram of the swarm aircraft in the method of the present invention. Figure 3 This image shows the change in the number of visible satellites for each spacecraft over flight time during the entire cluster flight process. The simulated images do not use the least squares method of the traditional Receiver Autonomous Integrity Monitoring (RAIM) algorithm (Method 1 of this invention) or the odd-even vector method of the traditional Receiver Autonomous Integrity Monitoring (RAIM) algorithm (Method 2 of this invention).

[0251] In the simulation experiment, a total of 5 aircraft were selected to verify the integrity monitoring performance of the method of the present invention. Without loss of generality, one aircraft was selected as the target aircraft, and the others as auxiliary aircraft to assist the target aircraft in fault detection and identification. In the actual application of the present invention, each aircraft is treated as a target aircraft and cooperates with other aircraft in the cluster. The simulation verification was conducted by flying for 3600 seconds under a constellation of 24 GPS satellites. In the simulation, the mean noise of the pseudorange measurement of the satellite receivers on each aircraft was 0, and the standard deviation was 5m. The mean noise of the ranging of the data link was 0, and the standard deviation was 3m. The false alarm rate of the fault detection of the entire navigation system was 1×10⁻⁶. -5 .

[0252] As shown in Table 1, satellite 3 was always visible to the entire cluster system within the range of 2200–2300 s. Injecting a pseudorange fault (0 m–120 m, with a step size of 10 m) into the pseudorange observations from each spacecraft to satellite 3 caused… Figure 4 It can be seen that by using the method of this invention and utilizing the common-mode pseudorange detection statistics constructed by all aircraft in the cluster system, the fault detection effect is far superior to that of traditional fault detection methods.

[0253] Table 1. Visible satellite information for each spacecraft from 2200 to 2300 s.

[0254]

[0255] Compare Figure 5 (a) and Figure 5 (b) During the 2200–2300 s interval, after a 40m fixed pseudorange fault was injected into satellite 3 in the GPS system, the traditional integrity monitoring algorithm, which uses an independent GNSS receiver, did not achieve ideal fault identification results. In the method of this invention, the cooperative nature of the aircraft in the cluster system is utilized, with all auxiliary aircraft also participating in fault identification. This allows faults that cannot be identified by the target aircraft alone in the navigation system to be identified by the auxiliary aircraft through common pseudorange faults. Comparison of the fault satellite identification results shows that the fault identification effect of the method of this invention is significantly better than that of the traditional integrity monitoring method.

[0256] Figure 6 (a) and Figure 6 (b) This is a pseudorange measurement error image of each spacecraft in the cluster system for different types of satellite pseudorange faults in the method of this invention. The simulation is set within 2200-2300s. A 30m fixed pseudorange fault is injected into the pseudorange observations of all spacecraft in the cluster system to satellite 3 to simulate a common pseudorange fault. A 30m fixed pseudorange fault is injected into the target spacecraft and auxiliary spacecraft 2 to simulate a specific pseudorange fault. Figure 6 (c) and Figure 6 (d) It can be seen that the detection rate of common pseudorange faults in the method of the present invention is slightly better than that of specific pseudorange faults. To comprehensively verify the fault detection performance, common pseudorange faults of size 30m were injected into each spacecraft to satellite No. 3 within 2200-2230s, and specific pseudorange faults of size 30m were injected into the target spacecraft and auxiliary spacecraft satellites No. 1 to No. 18 within 2700-2300s.

[0257] For the entire cluster system, spacecraft with no common-mode satellites and spacecraft with partial common-mode satellites are introduced separately. Figure 7 (a) and Figure 7(b) Comparison of visible satellite numbers before and after the introduction of two different common-mode satellites into the cluster system. Figure 7 (a) It can be seen that introducing a new spacecraft into the original cluster system results in 9 visible satellites within 500-600 seconds. The satellite numbers are: 5, 6, 9, 10, 11, 12, 14, 15, and 16. There are no satellites sharing the same mode with the original cluster system. Figure 7 (b) It can be seen that when a new spacecraft is introduced into the original cluster system, the number of visible satellites is 8 within 500-600 seconds. The satellite numbers are: 1, 4, 19, 20, 21, 22, 23, and 24. Among them, 6 satellites share the same operating mode as the original cluster system, accounting for 60% of the total. Figure 7 (c) A comparison of fault detection performance between the original cluster system and the system after introducing common-mode satellites under different conditions. Figure 7 (c) It can be seen that in a cluster system, the fewer the number of common-mode satellites between a certain aircraft and other aircraft, the greater the impact on the overall fault detection effect of the navigation system.

[0258] To further analyze the impact of the proportion of common-mode satellites on detection performance, when the simulation time is in the range of 500-600 seconds, the false alarm rate for fault detection in the simulation is set to 1×10⁻⁶. -5 The pseudorange measurement noise has a mean of 0 and a standard deviation of 5m. A fixed pseudorange fault of 30m is injected into satellite 3. At this point, the number of visible satellites for all swarm spacecraft is at its maximum. The 10 visible satellites of the swarm system are numbered 2, 3, 4, 17, 18, 19, 21, 22, 23, and 24. By setting simulation conditions and changing the visible satellite numbers of the auxiliary spacecraft in the swarm, the system simulates a complex environment where the visible satellites of different spacecraft are not the same due to building obstruction.

[0259] By varying the proportion of common-mode satellites between auxiliary and target aircraft within the cluster system, and comparing the average values ​​of fault detection statistics over 500–600 seconds, the performance of fault detection was analyzed. Tables 2 and 3 show that as the total number of visible satellites in the cluster system increases, the value of the fault detection statistics decreases, but the fault detection rate also increases, indicating that a higher number of visible satellites leads to better fault detection. When the total number of visible satellites is constant, the detection threshold remains the same. A higher proportion of common-mode satellites results in higher statistical detection volume and fault detection rate, leading to better fault detection performance.

[0260] Table 2 Fault detection statistics for different common-mode satellite proportions

[0261]

[0262] Table 3 Fault Detection Rates for Different Common-Mode Satellite Proportions

[0263]

[0264] As shown in the two sets of simulations above, when performing fault detection on a specific aircraft in a cluster system, and utilizing auxiliary aircraft in the cluster that contain common-mode satellites to assist in constructing detection statistics, the higher the proportion of common-mode satellites selected by the target aircraft from the auxiliary aircraft, the better the fault detection performance. Based on this characteristic, the method of this invention proposes an adaptive selection scheme to ensure that the fault detection performance of each aircraft in the cluster system reaches its optimal state.

[0265] The simulation setup includes a cluster system with six aircraft. One aircraft is selected for integrity monitoring, while the others serve as auxiliary aircraft. The false alarm rate for fault detection in the simulation is set to 1×10⁻⁶. -5 The noise of the pseudorange measurement has a mean of 0 and a standard deviation of 5m. Within 500-600s, a fixed pseudorange fault of 25m is injected into satellite 3. The number of visible satellites and the visible satellite numbers of each spacecraft remain unchanged. Table 4 shows the visible satellite status of each spacecraft in the cluster system between 500-600s.

[0266] Table 4. Visible satellite information for each aircraft in the cluster system from 500 to 600 seconds.

[0267]

[0268] A common-mode auxiliary aircraft screening and optimization algorithm was adopted, and auxiliary aircraft with different common-mode satellite ratios were added sequentially, resulting in 5 aircraft combinations. For ease of representation, the target aircraft is designated as TAG, and the auxiliary aircraft are represented by numbers 1 to 5. Table 5 shows the corresponding ratios for each group in the entire screening and optimization algorithm. As shown in Table 5, the fault detection performance is optimal after adding auxiliary aircraft with common-mode satellite ratios of 80%, 70%, and 60% respectively. However, adding an auxiliary aircraft with a fault detection rate of 50% actually reduces the fault detection performance.

[0269] Table 5 Performance metrics for fault detection in cluster systems

[0270]

[0271] Figure 7 (d) shows the change curve of fault detection performance fitted based on the limit ratio obtained from each set of experiments. Figure 7 (d) It can be seen that as more common-mode auxiliary aircraft are added, the fault detection performance first gradually improves, and then gradually decreases as the proportion of common-mode satellites in the added common-mode auxiliary aircraft decreases.

[0272] After completing the fault detection and elimination of satellite pseudorange, a cooperative detection statistic is established using the established cooperative navigation model to detect and identify faults in the observation information of the auxiliary sensors. The simulation sets the mean of the ranging noise of the data link to 0 and the standard deviation to 3m. The simulation time is 2200s to 2300s, and a fixed ranging fault of 25m is injected into the ranging information from the target spacecraft to the No. 2 auxiliary spacecraft. Figure 8 (a) is a fault detection image of the auxiliary sensor, from Figure 8 (a) It can be seen that by establishing a collaborative monitoring model, the navigation system can detect faults in auxiliary sensors. Figure 8 (b) It can be seen that the method of the present invention can detect the ranging fault of the auxiliary sensor and can identify the numbers of most of the auxiliary sensors that have abnormal measurement information.

[0273] Compared to traditional receiver-based autonomous integrity monitoring, which uses independent GNSS receivers to monitor for satellite pseudorange faults in the current navigation system, this invention employs a collaborative approach involving multiple GNSS receivers, sharing their respective measurement information. This significantly improves fault detection sensitivity, and for common-mode pseudorange faults, not only the target aircraft but also auxiliary aircraft participate in fault identification. This improves the fault identification rate while implementing decentralized fault handling, enhancing the integrity monitoring efficiency of the navigation system. Furthermore, this invention not only detects satellite pseudorange faults but also establishes a collaborative monitoring model for auxiliary sensors in the navigation system through sensor fusion. Once the system completes monitoring for satellite pseudorange faults, collaborative detection statistics are established to detect faults in the auxiliary sensor observations. If an auxiliary sensor fault is detected, it is identified through gross error detection, and the identified fault is then eliminated and isolated. This improves the overall integrity performance of the navigation system.

[0274] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0275] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for fault detection and identification of navigation system groups using collaborative navigation information co-mode combination, characterized in that, Includes the following steps: Step 1): Each aircraft in the cluster system uses a GNSS receiver to acquire satellite pseudorange observations and uses a data link as an auxiliary sensor to acquire relative distance information between aircraft. An observation equation is established based on the satellite pseudorange observations and relative distance observations. The number of swarm aircraft is One of the aircraft was the target aircraft. , At that time, the rest served as auxiliary aircraft. , ; Target aircraft and auxiliary aircraft The number of visible satellites are respectively and ; Step 1.1) Calculate the satellite pseudorange observations of each spacecraft within the cluster system to assist the spacecraft. The pseudorange observation equation is: ； in, For auxiliary aircraft Of all currently visible satellites, up to the [number]th Pseudorange measurements of visible satellites, , For auxiliary aircraft To satellite The true value of the distance. At the speed of light, For aircraft Clock error of airborne GNSS equipment, This includes satellite clock bias, ionospheric and tropospheric delays, as well as pseudorange noise caused by signal multipath and non-line-of-sight errors. Step 1.2), calculate the target aircraft To auxiliary aircraft The relative distance observation equation is: ； in, For target aircraft To auxiliary aircraft The relative distance measurement value, For target aircraft To auxiliary aircraft The true value of the relative distance, For target aircraft Clock bias of the airborne data link system For auxiliary aircraft Clock bias of the airborne data link system Measurement noise in the data link system; Step 2) Within the cluster network, each aircraft interacts with each other, shares its own observations, and each aircraft unit linearizes its observation equations and calculates the corresponding pseudorange residuals and relative range residuals. Step 2.1), calculate the auxiliary aircraft up to currently visible satellites geometric distance for: ； in, For auxiliary aircraft Visible satellites The position, in the three-dimensional coordinates of the Earth-Centered, Earth Fixed, ECEF coordinate system, is: , For auxiliary aircraft The position, in the three-dimensional coordinates of the Earth's fixed coordinate system, is: ; Step 2.2), the auxiliary aircraft To its visible satellites The geometric distance is expanded using a Taylor series and then linearized, resulting in the following expression: ； ； in, Auxiliary aircraft Position errors in the X, Y, and Z directions in the ECEF coordinate system , , Auxiliary aircraft With satellite The direction cosines in the X, Y, and Z directions, Determine the direction cosine of the position error of a visible star; Step 2.3), calculate the auxiliary aircraft To its visible satellites The difference between the calculated and measured distance values ​​is used to assist the aircraft. The pseudorange residual value is: ； Step 2.4), calculate the target aircraft With auxiliary aircraft The geometric distance is: ； in, For target aircraft The position, in the ECEF coordinate system, has three-dimensional position coordinates as follows: , For auxiliary aircraft The position, in the ECEF coordinate system, has three-dimensional position coordinates as follows: ; Step 2.5), target aircraft With auxiliary aircraft The geometric distance is expanded using a Taylor series and then linearized. The resulting expression is: ； ； in, Target aircraft Position errors in the X, Y, and Z directions in the ECEF coordinate system , , Auxiliary aircraft With target aircraft The direction cosines in the X, Y, and Z directions, Indicates support aircraft Calculate the direction cosine of the position error; Step 2.6), calculate the target aircraft To the aircraft relative distance residual for: ； Step 2.7), based on the auxiliary aircraft The pseudorange residuals are obtained from the target aircraft in the swarm system. pseudo-range residual for: ； in, For target aircraft Of all currently visible satellites, up to the [number]th Pseudorange measurements of visible satellites, , For target aircraft To its visible satellites The distance calculation value, , , Target aircraft With visible satellites The direction cosine in the X, Y, and Z directions; Step 3) Each spacecraft constructs its own observation matrix based on the visible satellites and distance information to other spacecraft, and establishes a combined model of pseudorange residual and relative distance residual. The measurement noise of the observations is normalized, and the state estimation solution of the target spacecraft is calculated. Step 3.1), Target aircraft and auxiliary aircraft Observation matrices are constructed based on the pseudorange observation information and relative distance information. and Among them, the target aircraft observation matrix Observation matrix of satellites and observation matrix of other auxiliary aircraft The composition is as follows: ； ； Step 3.2), Target aircraft It will be based on its connection to the auxiliary aircraft The relative distance residual and the target aircraft The pseudorange residuals are combined and modeled as follows: ； in, For target aircraft The distance residual observation, For the state variables of the target aircraft, Measurement noise of the target aircraft's onboard sensors; Step 3.3), select the weighting matrix. The variances of pseudorange measurement noise and measurement noise are normalized, and a weighted matrix is ​​used. The specific format is as follows: ； in, The variance of pseudorange observation noise. The variance of the ranging noise; Step 3.4), according to the weighted least squares principle, calculate the estimation error vector. When the sum of squares of the target aircraft reaches its minimum value Weighted least squares state estimation solution for: ； in, This is the weighted matrix corresponding to the target aircraft; Step 4): Each spacecraft broadcasts the number and quantity information of its visible satellites in the cluster network; the target spacecraft compares the number of its own visible satellites with the numbers of all visible satellites of each other spacecraft, and records the visible satellites with the same number; the visible satellites with the same number among the spacecraft are designated as the common-mode satellites of the spacecraft. Step 5) The target spacecraft selects all auxiliary spacecraft in the cluster system that share the same mode with it, calculates the pseudorange residual vector of each spacecraft based on the observation matrix of each spacecraft, and constructs the common mode pseudorange residual statistical detection quantity. Step 6) Optimize the common-mode pseudorange residual statistical detection quantity. Before the target aircraft performs fault detection, calculate in the navigation system which auxiliary aircraft to select to build common-mode detection statistics to make the current fault detection performance of the navigation system the best. Step 7), calculate the fault detection threshold of the target aircraft and compare it with the statistical detection value of its common-mode pseudorange residual to determine whether a pseudorange measurement fault has occurred. Step 8) If a fault exists, the target aircraft identifies and eliminates the fault. Once the system is free of pseudorange faults, it further determines whether the data link used in the cooperative navigation system has malfunctioned. Step 9) The target aircraft uses its combined model to construct its cooperative detection statistics, calculates its cooperative detection threshold, compares its cooperative statistical detection quantity with the cooperative detection threshold, and determines whether the relative distance information of the data link has failed. Step 10): If a data link measurement fault exists in the system, fault identification and isolation procedures will be performed.

2. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 1, characterized in that, The specific steps of step 4) are as follows: Step 4.1): Each spacecraft detects all its own visible satellite information and interacts within the cluster network, enabling each spacecraft to observe the visible satellite numbers and quantities of other spacecraft in real time; target spacecraft The set of visible satellite IDs is Assisted aircraft The set of visible satellite IDs is ; Step 4.2): The target spacecraft compares its own set of visible satellite IDs with the sets of visible satellite IDs of other auxiliary spacecraft. If there are identical visible satellite IDs, then the target spacecraft... and auxiliary aircraft The common-mode satellite will have its number stored in the target spacecraft. and auxiliary aircraft Common-mode satellite set In the formula, For target aircraft With auxiliary aircraft The number of common-mode satellites.

3. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 2, characterized in that, The specific steps of step 5) are as follows: Step 5.1), extract the information about the target aircraft from Step 3.3). State estimation solution of pseudorange observation for: ； in For target aircraft The pseudorange residual observation, For target aircraft The pseudorange observation weighting matrix; Step 5.2), calculate the target aircraft And auxiliary spacecraft with co-mode satellites The pseudorange residual vectors are as follows: ； ； in, , Target aircraft Auxiliary aircraft Pseudorange measurement noise; For auxiliary aircraft The pseudo-range weighted matrix; For auxiliary aircraft The pseudorange residual vector; Step 5.3), within the cluster network, the target aircraft Simultaneously acquire other auxiliary aircraft that share the same model. Pseudorange measurements, assisting aircraft The quantity is For any given moment, the target aircraft, in addition to its own pseudorange residual vector, In addition, it is possible to obtain The common-mode pseudorange residual vectors are denoted as... , Therefore, a common-mode pseudorange residual detection statistic is constructed in a collaborative environment. for: ； in, For all auxiliary spacecraft that share a common-mode satellite with the target spacecraft The pseudo-range weighted matrix.

4. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 3, characterized in that, Step 6) includes the following specific steps: Step 6.1): In the current navigation system, each spacecraft constructs the common-mode pseudorange residual vector based on the common-mode satellite set in step 4.2). Each auxiliary aircraft in the computing cluster system Relative to the target aircraft The proportion of common-mode satellites is: ； in, For auxiliary aircraft With target aircraft The proportion of common-mode satellites; Step 6.2): ​​Based on the calculated common-mode satellite ratio, match and screen the auxiliary spacecraft according to each auxiliary spacecraft. relative target aircraft The common-mode satellites are sorted in descending order of their proportions. When calculating the common-mode detection statistic, the auxiliary spacecraft with the highest proportion of common-mode satellites in the current spacecraft is included to assist in constructing the detection statistic. And calculate the corresponding detection threshold as : ； ； in, The total number of auxiliary aircraft used to construct detection statistics in the current system is given by the pseudorange residual vectors corresponding to the target aircraft and the selected auxiliary aircraft at this time. , , This is the pseudorange weighting matrix for auxiliary aircraft that participates in constructing common mode detection statistics in the auxiliary aircraft matching scheme. The false alarm rate is given by the system. The number of visible satellites for the selected auxiliary spacecraft. for The inverse cumulative distribution function of the distribution; Step 6.3), according to and Calculate the limit ratio of target aircraft fault detection after screening. for: ； Step 6.4), compare the current statistical limit ratio. Compared to the previous statistical ratio, if the current statistical ratio... If the overall limit ratio is greater than or equal to the previous one, proceed to step 6.2; otherwise, take the auxiliary aircraft selected by the overall limit ratio calculated in the previous one and obtain the maximum value of the overall limit ratio, which represents the best fault detection performance. Step 6.5) Select the auxiliary aircraft matching scheme corresponding to the maximum control ratio as the screening of common-mode auxiliary aircraft to assist the target aircraft. Perform fault detection according to the method in step 7).

5. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 4, characterized in that, The specific steps of step 7) are as follows: Step 7.1): Calculate the fault detection threshold for each aircraft based on the preset false alarm rate. The formula for calculating the fault detection threshold is as follows: ； in, For target aircraft The probability that there is no pseudorange fault. It has degrees of freedom. The probability density function of the chi-square distribution, The false alarm rate given by the system; Step 7.2) compare the common-mode pseudorange residual detection statistics. and fault detection threshold The size of the satellite is used to determine whether there is a satellite malfunction. > If this occurs, it indicates that the system has detected a satellite malfunction.

6. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 5, characterized in that, The specific steps of step 8) are as follows: Step 8.1): If a fault is detected in step 7.2), the fault is identified, and the target aircraft... To eliminate pseudorange faults, a fault identification statistic was constructed using the Balda data detection method. for: ； in, For target aircraft The pseudo-range residual vector of the first One element, The standard deviation of pseudorange observation noise. For target aircraft Residual sensitive matrix The Line number Column elements; Step 8.2), calculate the target aircraft The fault identification threshold is calculated using the following formula: ； in, Indicates target aircraft The probability that the identification statistic is greater than the fault detection threshold. For target aircraft The fault identification threshold value, Let be the probability density function of the standard normal distribution. For fault identification threshold, The number of visible satellites for the spacecraft; Step 8.3): Compare the pseudo-distance fault identification statistics calculated in step 8.1) with the fault identification threshold calculated in step 8.2), perform pseudo-distance fault identification, and isolate the identified faults. Step 8.4): After the pseudorange fault is detected, identified and eliminated, the combined model in step 3.1) is used to detect and identify the ranging fault of the data link according to step 9).

7. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 6, characterized in that, The specific steps of step 9) are as follows: Step 9.1), based on the target aircraft Combined model to calculate target aircraft Cooperative residual vector for: ； Where, let matrix For the cooperative residual sensitive matrix; Step 9.2), calculate the target aircraft according to the following formula. Cooperative residual weighted sum of squares : ； Step 9.3) Calculate the posterior weight error of the weighted sum of squares of the cooperative residuals. As a statistical measure of collaborative detection of target aircraft: ； Step 9.4): Calculate the cooperative fault detection threshold for each aircraft based on the false alarm rate given by the system. The formula for calculating the fault detection threshold is as follows: ； in, For target aircraft The probability that the relative distance information of the data link is fault-free. For target aircraft Collaborative fault detection threshold; Step 9.5): To facilitate a unified comparison, calculate the fault detection limit corresponding to the cooperative fault detection statistics of the target aircraft in Step 9.3). : ； in, For collaborative fault detection thresholds; Step 9.6): Compare the cooperative fault detection statistic calculated in Step 9.3) with the fault detection limit calculated in Step 9.5) to determine whether the target aircraft's data link relative ranging information has failed. If the data link ranging data of the target aircraft is positive, it indicates a fault; otherwise, there is no fault.

8. The method for fault detection and identification of navigation system groups based on the common-mode combination of cooperative navigation information according to claim 7, characterized in that, The specific steps of step 10) are as follows: Step 10.1): Based on the judgment result in Step 9), if a fault occurs, the fault is identified and isolated. The fault identification statistic is constructed using the least squares collaborative residual vector: ； in, Indicates target aircraft The first of the cooperative residual vectors One element, It is a target aircraft Weighted matrix No. Line number Column elements, Indicates target aircraft Collaborative residual sensitive matrix The Line number Column elements; Step 10.2), in the entire cooperative navigation system, since the number of auxiliary aircraft is... And the number of visible satellites for the target spacecraft is ,get A recognition statistic, based on a given false alarm rate. The false alarm rate for each identification statistic is... The formula for calculating the limit for fault identification in a data link is: ； in, Indicates target aircraft The probability that the identification statistic is greater than the fault detection threshold. For target aircraft The threshold value for data link fault identification. The probability density function is the standard normal distribution. Step 10.3): Based on the detection threshold calculated in step 10.2), compare each identification statistic. With recognition threshold The size of the target aircraft is used to determine its size. To which auxiliary aircraft's ranging data malfunctioned: If Then the target aircraft To auxiliary aircraft If the ranging data is faulty, the navigation system will further isolate the faulty ranging data link; otherwise, it indicates that there is no ranging data link fault.

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