Airborne multi-source fusion navigation dynamic integrity monitoring and warning method, device and medium

Abstract

The application discloses an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method and device and a medium, relates to the field of multi-source fusion navigation dynamic integrity monitoring and alarming, and comprises the following steps: firstly, calculating the separation combination of a multi-source fusion navigation system under a plurality of fault hypotheses; secondly, performing navigation source fault detection and isolation by using a PCA multivariate statistical analysis method according to KF-LS form solutions of the separation combinations; then, verifying the fault navigation source recovery by using a w-detection method for the isolated navigation source, and dynamically regulating and controlling effective navigation source information; finally, calculating the PCA principal component contribution rate under each fault hypothesis to be monitored based on an integrity risk dynamic allocation criterion, dynamically calculating the protection level, and realizing integrity monitoring and alarming. The application can realize dynamic integrity monitoring of airborne multi-source fusion navigation, and can generate an alarm prompt by calculating the system protection level in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of multi-source fusion navigation dynamic integrity monitoring and warning, in particular to an airborne multi-source fusion navigation dynamic integrity monitoring and warning method, device and medium. BACKGROUND

[0002] The construction of a comprehensive positioning, navigation and timing (PNT) system has become a national strategy. Multi-source fusion navigation technology is the only way to achieve a comprehensive PNT system. This technology is characterized by the comprehensive use of satellite navigation, ground radar, inertial navigation, visual navigation and other information sources. Through the fusion of multi-source data, the navigation system achieves high precision, high reliability and robustness. For multi-source fusion navigation, there are many redundant sensors available in the same application scenario or application carrier. The key is to provide high-precision, high-reliability and high-anti-interference navigation services for the carrier in complex practical application environments. In the actual application process, due to factors such as harsh environment, human interference and hardware aging, any navigation source may produce deviations and faults. If these deviations and faults are not monitored in time, the entire navigation system will be contaminated by deviation and fault data, which will further lead to a decrease in navigation accuracy and even failure of the entire navigation system. However, the traditional integrity monitoring and warning method is mainly aimed at satellite navigation systems. Although it can isolate satellite faults, the fault detection model established mainly relies on the chi-square distribution model, which cannot give a quantitative result of fault impact. In addition, for the calculation of protection level, the satellite navigation system reports a clear integrity risk probability value in the ARAIM algorithm, and the prior fault probability of each satellite is equal. However, for multi-source fusion navigation systems, the prior fault probability of each navigation source is not the same and there is no clear value requirement. It needs to be dynamically adjusted according to the effective situation of the navigation source. Therefore, how to establish a dynamic integrity monitoring and warning method with quantifiable fault impact and allocatable integrity risk will improve the integrity monitoring performance of multi-source fusion navigation and ensure that the multi-source fusion navigation system provides high-precision and high-reliability positioning services. SUMMARY

[0003] The purpose of the present application is to provide an airborne multi-source fusion navigation dynamic integrity monitoring and warning method, device and medium, which can solve the problem of limited integrity monitoring and warning capability caused by unquantified fault impact and dynamic adjustment of integrity risk in protection level calculation in the current multi-source fusion navigation fault detection model.

[0004] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0005] In a first aspect, the application provides a method for monitoring and warning the dynamic integrity of an airborne multi-source fusion navigation system, comprising:

[0006] According to the number of effective navigation sources and the number of simultaneously faulty navigation sources in the airborne multi-source fusion navigation system, determine the separation combinations under each fault hypothesis that need to be monitored; one number of simultaneously faulty navigation sources corresponds to one fault hypothesis; one fault hypothesis corresponds to multiple separation combinations; the separation combination refers to the combination of all effective navigation sources excluding the assumed faulty navigation sources; the assumed faulty navigation sources refer to any navigation source of the assumed number of simultaneously faulty navigation sources among the effective navigation sources; the effective navigation sources refer to the navigation sources remaining after removing the faulty navigation sources included in the isolation set from all navigation sources in the airborne multi-source fusion navigation system;

[0007] For each separation combination under each fault hypothesis, apply the PCA multivariate statistical analysis method to detect the faulty navigation sources in each separation combination according to the state estimation value in the Kalman filter least squares form of the separation combination, and include the detected faulty navigation sources in the isolation set;

[0008] If the isolation set is not empty, then according to the state calculation quantity of the current airborne multi-source fusion navigation system and the measurement value of the faulty navigation sources in the current isolation set, use the w-detection method to verify the recovery of the faulty navigation sources; if the recovery verification is passed, remove the faulty navigation sources that pass the recovery verification from the isolation set; if the recovery verification fails, return to the step of "according to the state calculation quantity of the current airborne multi-source fusion navigation system and the measurement value of the faulty navigation sources in the current isolation set, use the w-detection method to verify the recovery of the faulty navigation sources";

[0009] For each separation combination under each fault hypothesis that meets the dynamic integrity risk allocation criterion, calculate the PCA principal component contribution rate, use the PCA principal component contribution rate as a dynamic weight adjustment factor to calculate the protection level of the airborne multi-source fusion navigation system under each fault hypothesis that meets the dynamic integrity risk allocation criterion, and perform integrity warning according to the protection level; the dynamic integrity risk allocation criterion is determined according to the number of effective navigation sources and the preset prior fault probability corresponding to each effective navigation source; the PCA principal component contribution rate is calculated from the eigenvector matrix and eigenvalue matrix calculated when the PCA multivariate statistical analysis method is applied to detect the faulty navigation sources in each separation combination; the eigenvector matrix and the eigenvalue matrix refer to the eigenvector matrix and eigenvalue matrix of the position state quantity and the velocity state quantity.

[0010] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the airborne multi-source fusion navigation dynamic integrity monitoring and alarming method.

[0011] In a third aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the airborne multi-source fusion navigation dynamic integrity monitoring and alarming method.

[0012] According to the specific embodiments provided by the present application, the following technical effects are disclosed.

[0013] The present application provides an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method, device and medium. On the basis of the traditional integrity monitoring, PCA multivariate statistical analysis is introduced for navigation source fault detection and isolation. While detecting the faulty navigation source, the influence of the fault on the multi-source fusion navigation is quantified through the PCA principal component contribution rate. The navigation source recovery verification is performed according to the main system state value and the measurement value of the isolated navigation source, the dynamic regulation of the multi-source information is realized, and then for the fault hypothesis meeting the integrity risk allocation criterion, the protection level is calculated in combination with the PCA principal component contribution rate, so as to perform the integrity alarming, and the multi-source fusion navigation dynamic integrity monitoring and alarming is realized. The present application can improve the integrity monitoring performance of the multi-source fusion navigation, and solve the problem that the integrity monitoring and alarming capability is limited due to the unquantified fault influence of the fault detection model and the inability of the integrity risk in the protection level calculation to be dynamically adjusted. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0015] Figure 1 FIG. 1 is an application environment diagram of an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method according to an embodiment of the present application;

[0016] Figure 2 FIG. 2 is a flowchart of an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method according to an embodiment of the present application;

[0017] Figure 3 FIG. 3 is a detailed flowchart of an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method according to an embodiment of the present application;

[0018] Figure 4A structural schematic diagram of a computer device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0020] The above purposes, features and advantages of the present application will be more obvious and easy to understand. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0021] The airborne multi-source fusion navigation dynamic integrity monitoring and alarming method provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be set up separately, or integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the number of effective navigation sources and the number of simultaneous fault navigation sources in the airborne multi-source fusion navigation system to the server 104, and after receiving the number of effective navigation sources and the number of simultaneous fault navigation sources in the airborne multi-source fusion navigation system, the server 104 applies the PCA multivariate statistical analysis method to detect the fault navigation source in each isolated combination under each fault assumption according to the state estimation value of the Kalman filter least square form of the isolated combination, and the detected fault navigation source is included in the isolated set; If the isolated set is not empty, according to the state calculation amount of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation source in the current isolated set, the w-detection method is used to verify the recovery of the fault navigation source; If the recovery verification is passed, the fault navigation source that passes the recovery verification is removed from the isolated set; If the recovery verification fails, continue to perform the recovery verification; For each isolated combination under each fault assumption that meets the integrity risk dynamic allocation criterion, calculate the PCA principal component contribution rate, and use the PCA principal component contribution rate as a dynamic weight adjustment factor to calculate the protection level of the airborne multi-source fusion navigation system under each fault assumption that meets the integrity risk dynamic allocation criterion, and perform integrity alarm according to the protection level. The server 104 can feed back the obtained integrity dynamic monitoring and alarm data to the terminal 102. In addition, in some embodiments, the airborne multi-source fusion navigation dynamic integrity monitoring and alarm method can also be realized by the server 104 or the terminal 102 alone, such as the terminal 102 can directly calculate the multi-hypothesis isolated combination that needs to be monitored for the number of effective navigation sources and the number of simultaneous fault navigation sources in the airborne multi-source fusion navigation system and perform integrity dynamic monitoring and alarm, or the server 104 can calculate the multi-hypothesis isolated combination that needs to be monitored for the number of effective navigation sources and the number of simultaneous fault navigation sources in the airborne multi-source fusion navigation system from the data storage system and perform integrity dynamic monitoring and alarm.

[0022] Among them, the terminal 102 can be but not limited to various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0023] In an exemplary embodiment, asFigure 2 As shown, an airborne multi-source fusion navigation dynamic integrity monitoring and alarm method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation is as follows: Figure 2 As shown, the main components include airborne multi-source fusion navigation, multi-hypothesis separation and combination, fault detection and isolation, recovery verification, and integrity monitoring and alarm. Multi-source fusion navigation serves as the input source for dynamic integrity monitoring and alarm, and in this embodiment, it mainly includes inertial navigation, GPS navigation, BDS navigation, barometric altimeter, and terrain-aided navigation. Multi-hypothesis separation and combination is the primary object of integrity monitoring. Fault detection and isolation involves fault detection for each separation combination to identify the faulty navigation source, including navigation source fault detection and fault judgment. Recovery verification involves recovery verification of the navigation sources in the isolated set, including navigation source recovery verification and the isolation set itself. Integrity monitoring and alarm calculates the protection level based on integrity risk; if the alarm limit is exceeded, an integrity alarm is generated, including integrity risk allocation, protection level calculation, alarm judgment, and alarm generation. Figure 3 As shown, the method in this embodiment specifically includes the following steps 201 to 204.

[0024] Step 201: Based on the number of effective navigation sources and the number of hypothesized simultaneously faulty navigation sources in the airborne multi-source fusion navigation system, determine the separation combinations under each fault hypothesis to be monitored. One hypothesis corresponds to one number of simultaneously faulty navigation sources; one fault hypothesis corresponds to multiple separation combinations. The separation combination refers to the combination of all effective navigation sources excluding hypothesized faulty navigation sources. The hypothesized faulty navigation source refers to any navigation source among the effective navigation sources with a preset number of simultaneously faulty navigation sources. The effective navigation source refers to the remaining navigation sources after removing faulty navigation sources included in the isolation set from all navigation sources in the airborne multi-source fusion navigation system. Initially, no fault detection is performed, and the isolation set is empty. At this time, the number of effective navigation sources is the total number of navigation sources. As the algorithm continuously executes, faulty navigation sources are continuously detected, and faulty navigation sources are continuously verified to be recovered. The effective navigation sources are dynamically adjusted, for example, at time k, time k+1, etc., the number and type of effective navigation sources are continuously adjusted.

[0025] Specifically, based on the number N of effective navigation sources in the airborne multi-source fusion navigation system s And the number of simultaneously faulty navigation sources n, and the number of multiple hypothesis separation combinations N to be monitored. c The calculation is as follows:

[0026]

[0027] where ζ(*) is a factorial function.

[0028] In step 202, for each isolated combination under each fault hypothesis, a PCA multivariate statistical analysis method is applied to detect the fault navigation source in each isolated combination according to the state estimation value in Kalman filter least square form of the isolated combination, and the detected fault navigation source is included in the isolation set.

[0029] In step 203, if the isolation set is not empty, a w-detection method is used to verify the recovery of the fault navigation source according to the state calculation of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation source in the current isolation set; if the recovery verification is passed, the fault navigation source that passes the recovery verification is removed from the isolation set; if the recovery verification is not passed, the step of "using the w-detection method to verify the recovery of the fault navigation source according to the state calculation of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation source in the current isolation set" is returned.

[0030] In step 204, for each isolated combination under each fault hypothesis that meets the integrity risk dynamic allocation criterion, a PCA principal component contribution rate is calculated, the PCA principal component contribution rate is used as a dynamic weight adjustment factor to calculate the protection level of the airborne multi-source fusion navigation system under each fault hypothesis that meets the integrity risk dynamic allocation criterion, and an integrity warning is given according to the protection level; the integrity risk dynamic allocation criterion is determined according to the number of effective navigation sources and the preset prior fault probability corresponding to each effective navigation source; the PCA principal component contribution rate is calculated according to the eigenvector matrix and the eigenvalue matrix calculated when the PCA multivariate statistical analysis method is applied to detect the fault navigation source in each isolated combination; the eigenvector matrix and the eigenvalue matrix refer to the eigenvector matrix and the eigenvalue matrix of the position state quantity and the velocity state quantity.

[0031] For steps 203 and 204, when the isolation set is not empty, step 203 is executed, and then step 204 is continued to be executed; when the isolation set is empty, step 203 does not need to be executed, and step 204 needs to be executed. That is, step 204 will be executed regardless of whether the isolation set is empty or not.

[0032] By implementing steps 201 to 204 above, this invention, based on traditional integrity monitoring, introduces PCA multivariate statistical analysis to establish a navigation source fault detection and isolation model. While detecting faulty navigation sources, it quantifies the impact of faults on multi-source fusion navigation through PCA principal component contribution rates. Furthermore, it establishes a navigation source recovery verification model based on the main system state value and isolated navigation source measurements, achieving dynamic control of multi-source information. Then, based on the effective navigation source status of the system, it establishes an integrity risk allocation criterion and calculates the system protection level using contribution rates. When the protection level exceeds a set alarm limit, an integrity alarm is generated, realizing dynamic integrity monitoring and alarming for multi-source fusion navigation. This invention can improve the integrity monitoring performance of multi-source fusion navigation and solve the problems of limited integrity monitoring and alarm capabilities caused by unquantified fault impacts and the inability to dynamically adjust integrity risks in protection level calculations in fault detection models.

[0033] In another exemplary embodiment of this application, step 202, for each separation combination under each fault assumption, based on the state estimate of the Kalman filter least squares form of the separation combination, applies the PCA multivariate statistical analysis method to detect and isolate the fault navigation source in each separation combination, specifically including:

[0034] (21) Construct the state equations and measurement equations of the Kalman filter least squares form (KF-LS form) of the airborne multi-source fusion navigation system. Specifically:

[0035] X k|k-1 =ΦX k-1 +w k (2)

[0036] Z k =H k X k +v k (3)

[0037] Y k =C k X k +v zx (4)

[0038]

[0039] Wherein, formula (2) is the state equation in Kalman filter form; formula (3) is the measurement equation in Kalman filter form; and formula (4) is the measurement equation in KF-LS form.

[0040] In the formula, X k-1 X represents the state estimate of the airborne multi-source fusion navigation system at time k-1, including position error, attitude error, velocity error, and zero bias of the accelerometer and gyroscope; k|k-1is the state recursive estimation value at the kth moment, i.e., the state estimation value at the kth moment recursively calculated using the state estimation value at the k-1th moment; Φ is a state transition matrix; w k is a system noise matrix; Z k is the measurement value of the airborne multi-source integrated navigation system at the kth moment, i.e., the data measured by the sensors of each navigation source; H k is an observation matrix; v k is the measurement noise matrix of the measurement model in the form of KF; v x is the measurement noise matrix of the measurement model in the form of KF; v k|k-1 is the measurement noise matrix of the measurement model in the form of KF; v zx is the noise matrix of the measurement model in the form of KF-LS, and the corresponding covariance matrix is:

[0041]

[0042] In the formula, P k|k-1 is the state estimation covariance matrix.

[0043] (22) solving the state equation and the measurement equation of the Kalman filter least square form to obtain the state estimation value of the Kalman filter least square form of each separated combination under each fault hypothesis

[0044] For the separated combination c under any fault hypothesis, the weight matrix W c is set to zero at the position corresponding to the excluded navigation source (the navigation source assumed to be faulty), and the KF-LS separation solution of the combination is constructed as:

[0045]

[0046] In the formula, is the state estimation value at the kth moment, which is calculated based on formula (2) and formula (4); W c is the weight matrix.

[0047] (23) according to the position state quantity and the velocity state quantity of the airborne multi-source integrated navigation system in the sliding window, applying the PCA multivariate statistical analysis method to determine the principal component model of the airborne multi-source integrated navigation system under normal working condition; the principal component model includes the eigenvector matrix and the eigenvalue matrix calculated from the position state quantity and the velocity state quantity.

[0048] Taking the position and the velocity in the system state as the characteristics, the principal component model under the normal working condition of the system is established by statistically analyzing the position and the velocity state values of the main system in the sliding window as:

[0049]

[0050] In the formula, LN is the sliding window length; D X is the position and velocity state data within the sliding window; is the mean of the position and velocity state data; V D is the eigenvector matrix; S D is the eigenvalue matrix; eig(*) is a function of taking the eigenvalues and eigenvectors of a matrix; is the sorted eigenvector matrix; is the sorted eigenvalue matrix; sort(*) is a function of sorting from large to small.

[0051] (24) For each of the separate combinations, according to the state estimation value in the form of Kalman filter least square, and the eigenvector matrix and eigenvalue matrix, a Hotelling statistic is applied to calculate a fault detection statistic and a fault detection threshold value of each of the separate combinations.

[0052] For each of the separate combinations, a Hotelling statistic is used to establish a fault detection statistic d s and a fault detection threshold value T s for each of the separate combinations:

[0053]

[0054] wherein, L P is the number of selected principal components; F α (*) is a F distribution function; a is a confidence level; T represents transposition.

[0055] (25) If the fault detection statistic is greater than the detection threshold value, the navigation source excluded in the only separate combination with the fault detection statistic less than the fault detection threshold value is regarded as a fault navigation source.

[0056] If the fault detection statistic corresponding to the separate combination is greater than the fault detection threshold value, it indicates that the separate combination contains a fault navigation source, and by finding the navigation source excluded in the only combination with the fault detection statistic less than the threshold value as the fault navigation source, the navigation source is included in the isolation set; otherwise, the navigation source is not included in the isolation set.

[0057] In another exemplary embodiment of the present application, if the isolation set is not empty, step 203 is performed, and according to the state solution of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation source in the current isolation set, a w-detection method is used to verify the recovery of the fault navigation source, specifically including:

[0058] (31) constructing a least square form of a target measurement equation for each isolated navigation source; the target measurement equation is a measurement equation including a main system state in Kalman filter form and measurement information of the isolated navigation source; the isolated navigation source refers to any faulty navigation source in the isolated set; the main system state refers to a state solution of the airborne multi-source integrated navigation system.

[0059] For each isolated navigation source, a least square form of a measurement equation including a KF main system state (i.e. a state solution of the airborne multi-source integrated navigation system in Kalman filter form) and measurement information provided by the isolated navigation source is constructed:

[0060]

[0061] wherein, Z k,Nsource is a position and velocity measurement value provided by the isolated navigation source; R k,Nsource is a corresponding measurement noise variance matrix; H k,Nsource is a KF measurement matrix of the navigation source to be verified; X k,main is a KF estimated state of the airborne multi-source integrated navigation system, i.e. a state solution of the airborne multi-source integrated navigation system in Kalman filter form; P k,main is a corresponding state error covariance matrix.

[0062] (32) bringing the position and velocity measurement values of the isolated navigation source and the main system state into the least square form of the target measurement equation, and calculating a verification detection statistic and a verification detection threshold by using a w-detection method.

[0063] Under the least square form, a verification detection statistic w Nsource and a verification detection threshold δ Nsource are respectively:

[0064]

[0065] wherein, e v is a unit vector; P zz,k is a measurement residual covariance matrix under the least square form, which is calculated according to the solution of formula (2) and formula (12), is a critical abnormal value, which depends on a significance level α.

[0066] (33) if the verification detection statistic is less than the detection threshold for a continuous preset value ε times, the recovery verification of the current isolated navigation source is passed.

[0067] If the verification detection statistic w Nsource is less than the detection threshold δ NsourceIf yes, the verification is passed, and the multi-source fusion navigation solution model can be re-integrated; otherwise, the verification is failed, and the verification is continued.

[0068] In another exemplary embodiment of the present application, in step 204, for each separated combination under each fault assumption satisfying the integrity risk dynamic distribution criterion, the PCA principal component contribution rate is calculated, and the protection level of the airborne multi-source fusion navigation system under each fault assumption satisfying the integrity risk dynamic distribution criterion is calculated by taking the PCA principal component contribution rate as a dynamic weight adjustment factor, and the integrity warning is given according to the protection level, specifically including:

[0069] (41) The integrity risk dynamic distribution ratio index under each fault assumption is calculated according to the number of effective navigation sources, and the preset prior fault probability and the integrity risk probability.

[0070] According to the effective navigation source condition, the preset integrity risk budget and the preset prior fault probability, the integrity risk dynamic distribution criterion is established as:

[0071]

[0072] In the formula, is the integrity risk probability under the fault assumption H g to be met by the following. g is the preset prior fault probability under the fault assumption H g g is the number of simultaneously failed navigation sources; and the maximum value of g is the number of current effective navigation sources. g is the integrity risk dynamic distribution ratio index under the fault assumption H g .

[0073] (42) If the integrity risk dynamic distribution ratio index is less than a preset ratio value, it is considered that each separated combination under the corresponding fault assumption needs to be monitored, otherwise, each separated combination under the corresponding fault assumption is not monitored.

[0074] For the assumption case where R g is less than a preset ratio value, for example, the fault assumption case where R g is less than 1.

[0075] (43) For each separated combination under a fault assumption to be monitored, the PCA principal component contribution rate is calculated according to the state estimation value in the Kalman filter least square form of the separated combination, and the corresponding elements in the characteristic vector matrix and the characteristic value matrix. The PCA principal component contribution rate is taken as a dynamic weight adjustment factor.

[0076] For each combination to be monitored, the PCA principal component contribution rate is calculated as a dynamic weight adjustment factor γm,c is:

[0077]

[0078] where γ m,c represents the PCA principal component contribution rate; L P is the number of selected principal components; represents the lth column eigenvector in the eigenvector matrix; represents the element in the jth row and lth column of the eigenvector matrix; represents the element in the lth row and lth column of the eigenvalue matrix; represents the jth state quantity in the Kalman filter least squares form state estimation value of the separation combination c at time k; represents the Kalman filter least squares form state estimation value of the separation combination c at time k; J N is the number of state quantities of the separation combination c under the fault hypothesis to be monitored.

[0079] (45) For each separation combination under each fault hypothesis to be monitored, the separation fault detection threshold of each separation combination under each fault hypothesis to be monitored is calculated according to the dynamic weight adjustment factor, the preset prior fault probability, and the estimation error standard deviation of the position state solution quantity.

[0080]

[0081] wherein, represents the fault hypothesis H m to be monitored; N ss,m represents the fault hypothesis H m to be monitored; γ m,c represents the PCA principal component contribution rate; represents the estimation position error standard deviation maximum value of the separation combination c under the fault hypothesis H m to be monitored; Q() represents the right tail function of the standard normal distribution.

[0082] (46) According to the protection level of the airborne multi-source fusion navigation system under the fault-free hypothesis, the protection level under each fault hypothesis to be monitored, and the separation fault detection threshold of each separation combination under each fault hypothesis to be monitored, a protection level inequality is constructed, specifically:

[0083]

[0084] wherein,

[0085] wherein, PL m represents the fault hypothesis H mP(H0) represents the protection level under the no-fault condition; H0 is the no-fault hypothesis; σ0 is the estimated position error standard deviation under the no-fault condition; P(H m ) represents the preset prior fault probability of the fault hypothesis H m to be monitored; σ m is the maximum value of the estimated position error standard deviation in all separate combinations of the fault hypothesis H m to be monitored; T ss,m is the maximum value of the separate fault detection threshold T m in all separate combinations of the fault hypothesis H to be monitored; I REQ represents the allocated integrity risk, which is the integrity risk probability of the system as a whole, and can be considered as the sum of the integrity risk probabilities under each fault hypothesis in formula (15).

[0086] (47) The protection level inequality is solved by using the half-interval search method, and the protection level of the airborne multi-source fusion navigation system under each fault hypothesis to be monitored is calculated.

[0087] (48) The maximum value of the protection level of the airborne multi-source fusion navigation system under each fault hypothesis to be monitored is taken as the final protection level. That is, PL = max{PL m}.

[0088] (49) If the final protection level is greater than the alarm limit value, integrity alarm is prompted.

[0089] The calculated protection level PL is compared with the set alarm limit value AL. If the protection level PL is greater than the alarm limit value AL, an alarm prompt needs to be sent to the user in time; otherwise, no alarm prompt is generated.

[0090] In this embodiment, the airborne multi-source fusion navigation dynamic integrity monitoring can be realized, the fault navigation source is detected, isolated and verified, the navigation source information is dynamically regulated, the protection level of the system is calculated in real time to generate an alarm prompt, and thus the reliability of the multi-source fusion navigation system is improved. In addition, the present application does not need to increase additional hardware cost for the existing multi-source fusion navigation system, only needs to upgrade the algorithm, introduces the dynamic integrity monitoring and alarm module, and thus the detection and isolation of the fault navigation source, the recovery verification of the isolated navigation source, and the dynamic allocation of the integrity risk according to the navigation source condition to calculate the protection level can be realized, and thus the reliability of the multi-source fusion navigation system is improved.

[0091] The application further provides an application scenario of the airborne multi-source fusion navigation dynamic integrity monitoring and alarming method. Specifically, the airborne multi-source fusion navigation dynamic integrity monitoring and alarming method provided in the embodiment can be applied in a flight scenario with complex terrain environment and strong interference. The scenario includes flight route planning, navigation positioning, and flight task execution, etc. The airborne multi-source fusion navigation dynamic integrity monitoring and alarming method provided in the embodiment belongs to the navigation positioning link.

[0092] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal. An internal structure diagram of the computer device can be as shown in Figure 4 The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store airborne multi-source fusion navigation dynamic integrity monitoring and alarming data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement an airborne multi-source fusion navigation dynamic integrity monitoring and alarming method.

[0093] Those skilled in the art can understand that Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0094] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0095] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0096] In an example embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0097] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0098] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0099] The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0100] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, it should be understood that the application encompasses all possible combinations of the technical features unless such a combination is not technically possible.

[0101] The principles and implementation manners of the present application are described herein by using specific examples, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, according to the idea of the present application, the specific implementation manners and application scopes will be changed by those skilled in the art. In conclusion, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. An airborne multi-source fusion navigation dynamic integrity monitoring and warning method, characterized in that, The application relates to a method for monitoring and isolating fault navigation sources in an airborne multi-source fusion navigation system. According to the number of effective navigation sources and the number of assumed simultaneous fault navigation sources in the airborne multi-source fusion navigation system, a separated combination under each fault assumption needing to be monitored is determined; one assumed simultaneous fault navigation source corresponds to one fault assumption; one fault assumption corresponds to multiple separated combinations; the separated combination refers to a combination of all effective navigation sources excluding the assumed fault navigation sources; the assumed fault navigation sources refer to any navigation source of the assumed simultaneous fault navigation source number in the effective navigation sources; The effective navigation sources refer to the navigation sources remaining after removing the fault navigation sources in the isolation set from all navigation sources in the airborne multi-source fusion navigation system; For each separated combination under each fault assumption, according to the state estimation value of the Kalman filter least square form of the separated combination, a PCA multivariate statistical analysis method is applied to detect the fault navigation sources in each separated combination, and the detected fault navigation sources are included in the isolation set; If the isolation set is not empty, according to the state solution quantity of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation sources in the current isolation set, a w-detection method is used to verify the recovery of the fault navigation sources; If the recovery verification is passed, the fault navigation sources passing the recovery verification are removed from the isolation set; If the recovery verification fails, the step of "according to the state solution quantity of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation sources in the current isolation set, using the w-detection method to verify the recovery of the fault navigation sources" is returned; For each separated combination under each fault assumption meeting the integrity risk dynamic allocation criterion, the PCA principal component contribution rate is calculated, the PCA principal component contribution rate is taken as a dynamic weight adjustment factor to calculate the protection level of the airborne multi-source fusion navigation system under each fault assumption meeting the integrity risk dynamic allocation criterion, and an integrity alarm is given according to the protection level; The integrity risk dynamic allocation criterion is determined according to the number of effective navigation sources and the preset prior fault probability corresponding to each effective navigation source; the PCA principal component contribution rate is calculated according to the characteristic vector matrix and the characteristic value matrix calculated when the PCA multivariate statistical analysis method is applied to detect the fault navigation sources in each separated combination; The characteristic vector matrix and the characteristic value matrix refer to the characteristic vector matrix and the characteristic value matrix of the position state quantity and the velocity state quantity.

2. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 1, characterized in that, For each separated combination under each fault assumption, according to the state estimation value of the Kalman filter least square form of the separated combination, a PCA multivariate statistical analysis method is applied to detect and isolate the fault navigation sources in each separated combination, and the method specifically comprises the following steps: The state equation and the measurement equation of the Kalman filter least square form of the airborne multi-source fusion navigation system are constructed; The state equation and the measurement equation of the Kalman filter least square form are solved to obtain the state estimation value of the Kalman filter least square form of each separated combination under each fault assumption; According to the position state quantity and the velocity state quantity of the airborne multi-source fusion navigation system in the sliding window, a PCA multivariate statistical analysis method is applied to determine a principal component model of the airborne multi-source fusion navigation system in a normal working condition; the principal component model includes a characteristic vector matrix and a characteristic value matrix calculated from the position state quantity and the velocity state quantity; For each of the separated combinations, according to the state estimation value in the least square form of the Kalman filter, and the characteristic vector matrix and the characteristic value matrix, a Hotelling statistic is applied to calculate a fault detection statistic and a fault detection threshold value of each of the separated combinations; If the fault detection statistic is greater than the detection threshold value, the excluded navigation source in the only separated combination with the fault detection statistic less than the fault detection threshold value is regarded as a fault navigation source.

3. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 2, characterized in that, The expression of the fault detection statistic is: In the formula, ds is a fault detection statistic; is a Kalman filter least square form state estimation value of the separation combination c at time k; is an eigenvalue matrix sorted based on eigenvalue size; is an eigenvector matrix sorted based on eigenvalue size; T represents transposition; The expression of the fault detection threshold value is: In the formula, T s is a detection threshold; L P is the number of selected principal components; L N is a sliding window length; F α (*) is a F distribution function; and α is a confidence level.

4. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 1, characterized in that, According to the state calculation quantity of the current airborne multi-source fusion navigation system and the measurement value of the fault navigation source in the current isolated set, a w-detection method is used for recovery verification of the fault navigation source, specifically including: For each isolated navigation source, a least square form of a target measurement equation is constructed; the target measurement equation is a measurement equation including the state of the main system in the form of the Kalman filter and the measurement information of the isolated navigation source; the isolated navigation source refers to any fault navigation source in the isolated set; the state of the main system refers to the state calculation quantity of the airborne multi-source fusion navigation system; The position measurement value and the velocity measurement value of the isolated navigation source and the state of the main system are brought into the least square form of the target measurement equation, and a w-detection method is used to calculate a verification detection statistic and a verification detection threshold value; If the verification detection statistic is less than the detection threshold value for a continuous preset number of times, the recovery verification of the current isolated navigation source is passed.

5. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 4, characterized in that, The expression of the verification detection statistic is: wherein In the formula, w Nsource represents the verification statistics; e v is a unit vector; P zz,k is a measurement residual covariance matrix in the least squares form; Z k,Nsource is a position measurement value and a speed measurement value provided by the isolated navigation source; X k,main is a state solution of the Kalman filter form of the airborne multi-source fusion navigation system; R k,Nsource is a measurement noise variance matrix corresponding to the isolated navigation source; P k,main is a state error covariance matrix corresponding to the airborne multi-source fusion navigation system; The expression of the verification detection threshold value is: In the formula, is a critical abnormal value.

6. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 1, characterized in that, For each separated combination under each fault assumption that meets the dynamic allocation criterion of integrity risk, a PCA principal component contribution rate is calculated, the PCA principal component contribution rate is taken as a dynamic weight adjustment factor to calculate the protection level of the airborne multi-source fusion navigation system under each fault assumption that meets the dynamic allocation criterion of integrity risk, and an integrity warning is given according to the protection level, specifically including: According to the number of effective navigation sources, and a preset prior fault probability and an integrity risk probability, a dynamic allocation ratio index of integrity risk under each fault assumption is calculated; If the dynamic allocation ratio index of integrity risk is less than a ratio preset value, it is considered that each separated combination under the corresponding fault assumption needs to be monitored, otherwise, each separated combination under the corresponding fault assumption is not monitored; For each separated combination under each fault assumption to be monitored, the PCA principal component contribution rate is calculated according to the state estimation value in the least square form of the Kalman filter of the separated combination, and corresponding elements in the corresponding characteristic vector matrix and the corresponding characteristic value matrix; The PCA principal component contribution rate is taken as a dynamic weight adjustment factor; For each separation combination under each monitored fault hypothesis, a separation fault detection threshold of the separation combination under the monitored fault hypothesis is calculated according to the dynamic weight adjustment factor, the preset prior fault probability and the estimated error standard deviation of the position state solution; A protection level inequality is constructed according to the protection level of the airborne multi-source fusion navigation system under the no-fault hypothesis, the protection level under each monitored fault hypothesis and the separation fault detection threshold of the separation combination under each monitored fault hypothesis; The protection level inequality is solved by using a half-interval search method to obtain the protection level of the airborne multi-source fusion navigation system under each monitored fault hypothesis; The maximum of the protection levels of the airborne multi-source fusion navigation system under each monitored fault hypothesis is taken as the final protection level; If the final protection level is greater than an alarm limit value, a perfectness alarm prompt is performed.

7. The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 6, characterized in that, An expression of the PCA principal component contribution rate is: wherein γ m,c represents the PCA principal component contribution rate; L P is the number of selected principal components; represents the lth column eigenvector in the eigenvector matrix; represents the element in the jth row and lth column of the eigenvector matrix; represents the element in the jth row and jth column of the eigenvalue matrix; represents the jth state quantity in the Kalman filter least square form state estimation value of the separation combination c at the kth moment; represents the Kalman filter least square form state estimation value of the separation combination c at the kth moment; J N is the number of state quantities of the separation combination c under the fault hypothesis to be monitored. 8.The airborne multi-source fusion navigation dynamic integrity monitoring and warning method according to claim 6, characterized in that, An expression of the final protection level is: PL = max{PL m}; wherein PL denotes the final protection level; PL m denotes the protection level under the fault hypothesis H m to be monitored; P(H0) denotes the protection level under the no-fault hypothesis; H0 is the no-fault hypothesis; σ0 is the estimated standard deviation of the position error under the no-fault condition; P(H m ) denotes the preset prior fault probability under the fault hypothesis H m to be monitored; σ m is the maximum value of the estimated standard deviation of the position error in all the separation combinations under the fault hypothesis H m to be monitored; T ss,m is the maximum value of the separation fault detection threshold in all the separation combinations under the fault hypothesis H m to be monitored; I REQ denotes the assigned integrity risk; denotes the separation fault detection threshold of the separation combination c under the fault hypothesis H m to be monitored; N ss,m denotes the number of all the separation combinations under the fault hypothesis H m to be monitored; γ m,c denotes the PCA principal component contribution rate; denotes the maximum value of the estimated standard deviation of the position error in the separation combination c under the fault hypothesis H m to be monitored; Q() denotes the right tail function of the standard normal distribution.

9. A computer device comprising: A memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the airborne multi-source fusion navigation dynamic integrity monitoring and alarm method in any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the airborne multi-source fusion navigation dynamic integrity monitoring and alarm method in any one of claims 1-8.

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