A Method and Device for Fusing Relative Navigation Information of a Data Link

Abstract

The present application discloses a method and apparatus for fusing relative navigation information of a data link. First, a relative navigation information fusion state equation is established based on input information. Secondly, a relative navigation information fusion measurement equation is established based on the input information to ensure the relative space-time reference under the condition of GNSS denial. Based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, a corresponding Kalman filter is selected according to the measurement information to perform relative navigation information fusion to obtain a navigation result. After completing the state estimation of one round of relative navigation information fusion, the navigation error state is corrected according to the measurement information, and the corrected relative positioning result is reported to the airborne avionics system. By fusing different types of measurement information and using the Kalman filter to optimally fuse this information, the accuracy and reliability of relative positioning are improved, the measurement information is quickly processed in a real-time environment, and the robustness and stability of the system are maintained by correcting the navigation error state.

Description

Technical Field

[0001] The present application relates to the field of avionics communication technology, and more particularly, to a method and apparatus for fusing relative navigation information of a data link. Background Art

[0002] With the continuous progress and improvement of information technology, the data link plays a central nervous role in related fields by virtue of its powerful information support capabilities, and is highly favored for providing real-time situation sharing and efficient collaboration capabilities. A data link is a data communication system that links each operating platform according to a unified message format and communication protocol and exchanges key information. Its functions are not limited to the interaction between platforms, but also include voice and data communication, status monitoring, and information sharing such as images between different systems.

[0003] The relative position relationship between members is one of the most critical pieces of information in collaborative tasks. When external navigation is available, the relative positions between groups are usually obtained by distributing positioning information through the data link. However, due to the limitations of external signals, it is vulnerable to interference in complex electromagnetic environments, resulting in difficulties in ensuring the accuracy of the spatio-temporal reference based on traditional positioning information. To solve this problem, a positioning method constructed by combining the characteristics of platform sensors through collaborative measurement capabilities has become an effective solution. This technology is based on precise network time synchronization, enabling each member to perform mutual measurements and obtaining relative position data using geometric positioning principles. In addition, each member within the system can not only complete its own positioning but also share relevant data with other members in the entire network to ensure information exchange.

[0004] To improve the accuracy and reliability of positioning results, a multi-source information fusion method is usually adopted to achieve combined positioning. This method performs position calculation in a relative coordinate system by integrating multi-dimensional information (such as distance, motion state, altitude data), aiming to construct a local spatio-temporal reference and output coordinate parameters.

[0005] The relative navigation technology based on the data link is mainly divided into two major systems: relative navigation based on state sharing and relative navigation based on measurement sharing. The two are distinguished according to the different core navigation information shared. The scheme based on state sharing requires less communication volume but has relatively lower navigation accuracy; while the scheme based on measurement sharing can obtain higher positioning accuracy, but requires more communication resources and has limited versatility. In current relative navigation information fusion algorithms, each member only models its own absolute navigation error and fuses the received state estimation pseudo-range measurements to correct its own navigation error, but this may lead to unstable or even divergent estimation, and overconfidence in ignoring source errors is not conducive to the rapid recovery of absolute navigation accuracy. Summary of the Invention

[0006] The purpose of this application is to overcome the existing technical defects and provide a method and device for fusing relative navigation information of a data link. By using a Kalman filter to optimally fuse this information, the accuracy and reliability of relative positioning are improved, the measurement information can be quickly processed in a real-time environment, and the robustness and stability of the system are maintained by continuously correcting the navigation error state.

[0007] The purpose of this application is achieved through the following technical solutions:

[0008] In a first aspect, this application proposes a method for fusing relative navigation information of a data link, and the method includes:

[0009] Establish a relative navigation information fusion state equation based on the input information, including an absolute navigation error state equation for the navigation controller, an absolute navigation error state equation for non-navigation controllers, and a relative navigation error state equation;

[0010] Establish a relative navigation information fusion measurement equation based on the input information, obtain measurement information according to the relative navigation information fusion measurement equation, and ensure the relative spatio-temporal reference under the condition of GNSS denial. The relative navigation information fusion measurement equation includes a round-trip loop timing measurement equation, a geographical pseudorange measurement equation, and a grid pseudorange measurement equation;

[0011] Based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, select the corresponding Kalman filter according to the measurement information to perform relative navigation information fusion to obtain a navigation result;

[0012] After completing the state estimation of one round of relative navigation information fusion, correct the navigation error state according to the measurement information and report the corrected relative positioning result to the airborne avionics system.

[0013] In a possible implementation manner, the absolute navigation error state equation of the navigation controller is: , where is the time derivative of the NC system state vector, is the state equation of NC in the absolute navigation state, is the NC system state vector, is the system noise driving matrix, is the system noise sequence.

[0014] In a possible implementation manner, the round-trip loop timing measurement equation is , where is the clock offset of node i, is the clock offset of NC, is the measurement noise;

[0015] The geographical pseudorange measurement equation is:

[0016] , where , , are the estimated geodetic height, longitude and latitude of node i respectively, , , are the geodetic height, longitude and latitude errors of node i respectively, , , are the estimated values of the geodetic height, longitude and latitude errors of node i respectively, is the clock error of i relative to NC, is the measurement noise of the overall geometric pseudorange, is the first parameter;

[0017] The grid pseudorange measurement equation is:

[0018] , where , , are the grid position errors of node i; , , are the estimated values of the grid position errors of node i; , , are the grid position errors of node j; , , are the estimated values of the grid position errors of node j; is the random measurement noise, is the first matrix.

[0019] In a possible implementation manner, when establishing the relative navigation information fusion measurement equation, the source quality information in the message transmitted by the data link is combined, and the source quality information is converted into measurement noise.

[0020] In a possible implementation manner, the functional software formed by the relative navigation information fusion method will reside at the data link terminal in the form of software configuration items.

[0021] In a possible implementation manner, the input information includes the local inertial navigation solution result, PPLI message, pseudorange information, and RTT relative clock difference. The local inertial navigation solution result includes position information, velocity information, and attitude information. The PPLI message includes the absolute navigation information of other aircraft, relative navigation information, absolute navigation quality, and relative navigation quality information.

[0022] In a possible implementation, the navigation result includes absolute position information, absolute position quality, relative position information, and relative position quality. The absolute position information includes the inertial navigation longitude, latitude, and altitude information corrected by the relative navigation information. The absolute position quality information is calculated from the filtering covariance matrix in the relative navigation information fusion. The relative position information includes U, V, and W coordinate information. The relative position quality information is calculated from the filtering covariance matrix in the relative navigation information fusion.

[0023] In a possible implementation, the position of the sea level corresponding to the true absolute position of the navigation controller is used as the origin of the grid coordinate system.

[0024] The geographical coordinate system where the origin is located is rotated by an angle θ around the celestial axis, and the new three-axis directions are used as the U, V, and W axes of the grid coordinate system. The angle θ is the heading angle error output by the inertial navigation of the navigation controller.

[0025] After rotating by the angle θ around the celestial axis, the V axis of the grid coordinate system deviates from the local north direction, and the U axis deviates from the local east direction, so that the true value of the heading of the navigation controller in the grid coordinate system is equal to the erroneous heading indicated by the inertial navigation of the navigation controller.

[0026] In a second aspect, the present application proposes a data link relative navigation information fusion device, which includes:

[0027] A state equation establishment module for establishing a relative navigation information fusion state equation according to the input information, including an absolute navigation error state equation for the navigation controller, an absolute navigation error state equation for non-navigation controllers, and a relative navigation error state equation.

[0028] A measurement equation establishment module for establishing a relative navigation information fusion measurement equation according to the input information to ensure the relative spatio-temporal reference under the condition of GNSS denial. The relative navigation information fusion measurement equation includes a round-trip loop timing measurement equation, a geographical pseudorange measurement equation, and a grid pseudorange measurement equation.

[0029] A navigation result generation module for performing relative navigation information fusion based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, and selecting a corresponding Kalman filter according to the measurement information to obtain a navigation result.

[0030] An error correction module for correcting the navigation error state according to the measurement information after completing the state estimation of one round of relative navigation information fusion, and reporting the corrected relative positioning result to the airborne avionics system.

[0031] The main solution of the present application and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and claimed in the present application; moreover, in the present application, (each non-conflicting alternative) alternatives can be freely combined with each other and with other alternatives. Those skilled in the art can understand that there are various combinations according to the prior art and common general knowledge after understanding the solutions of the present application, and all of them are the technical solutions to be protected by the present application, and will not be enumerated here.

[0032] The present application discloses a method and device for fusing relative navigation information of a data link. First, a relative navigation information fusion state equation is established according to input information. Secondly, a relative navigation information fusion measurement equation is established according to input information to ensure the relative space-time reference under the condition of GNSS denial. Based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, a corresponding Kalman filter is selected according to measurement information to perform relative navigation information fusion to obtain a navigation result. After completing the state estimation of one round of relative navigation information fusion, the navigation error state is corrected according to measurement information, and the corrected relative positioning result is reported to the airborne avionics system. By fusing different types of measurement information and using the Kalman filter to optimally fuse this information, the accuracy and reliability of relative positioning are improved, the measurement information can be quickly processed in a real-time environment, and the robustness and stability of the system are maintained by continuously correcting the navigation error state. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 FIG. shows a schematic flow chart of a method for fusing relative navigation information of a data link proposed in an embodiment of the present application.

[0035] Figure 2 FIG. shows a schematic diagram of input information and a navigation result proposed in an embodiment of the present application.

[0036] Figure 3 FIG. shows a schematic diagram of the filter adopted for relative navigation information fusion for different measurement information and the state variables that should be corrected in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following describes the implementation manners of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0038] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0039] In the prior art, under the condition of GNSS denial, when the spatio-temporal reference established based on GNSS is challenged, the data-link relative navigation technology can establish a local spatio-temporal reference for the cluster and provide a backup means for relative positioning and relative timing for the cluster. The relative navigation information fusion method essentially belongs to distributed information fusion and is one of the key technologies directly determining the accuracy and stability of relative navigation fusion. Therefore, it is necessary to conduct systematic research on it.

[0040] Therefore, in order to solve the problems of unstable information fusion and overconfidence caused by ignoring the navigation source error in the existing information fusion methods, the embodiments of the present application propose a data-link relative navigation information fusion method and device. By fusing different types of measurement information and using a Kalman filter to optimally fuse this information, the accuracy and reliability of relative positioning are improved. It can quickly process measurement information in a real-time environment and maintain the robustness and stability of the system by continuously correcting the navigation error state. Next, it will be described in detail.

[0041] Please refer to Figure 1 , Figure 1 , which shows a schematic flow chart of a data-link relative navigation information fusion method proposed by the embodiments of the present application. It is derived from the actual requirements of cluster relative navigation under the condition of GNSS denial for an aviation platform. The purpose is to improve several problems caused by ignoring the navigation source error in the existing theoretical research through the designed relative navigation information fusion method, and at the same time provide theoretical support for the engineering practice of relative navigation algorithms. The method includes the following steps:

[0042] Step S1: Establish a relative navigation information fusion state equation according to the input information, including an absolute navigation error state equation for the navigation controller, an absolute navigation error state equation for non-navigation controllers, and a relative navigation error state equation.

[0043] In addition to considering its own absolute navigation error, the ordinary members in the state equation also consider the relative navigation error with the NC to ensure the stability of the relative navigation information fusion. Therefore, for the Navigation Controller (NC), a state equation containing only 11-dimensional absolute navigation errors is established, and for non-navigation controllers, a state equation containing 11-dimensional absolute navigation error states and 7-dimensional relative navigation error states is established. The absolute navigation error state equation of the navigation controller is as follows: , where is the time derivative of the NC system state vector, is the state equation of the NC in the absolute navigation state, is the NC system state vector, is the system noise driving matrix, is the system noise sequence.

[0044] First, it is considered in two types: NC and non-NC. The navigation state of the node NC is only composed of the absolute navigation state. For the absolute navigation error state equation of the navigation controller, where: , the subscript NC indicates that this item is the navigation state corresponding to the NC, and the superscript a indicates the absolute navigation state; , and are the longitude error, latitude error, and altitude error respectively; , and are the eastward velocity error, northward velocity error, and upward velocity error respectively; , and are the pitch angle error, roll angle error, and heading angle error respectively; and are the local clock offset and clock drift respectively, is the state equation of the NC.

[0045] The specific form of is: , , , , , , , , The specific forms are respectively: , , , , , , , , , in the above formula and represent the latitude and altitude of NC, 、 and represent the eastward velocity, northward velocity and upward velocity of NC respectively, is the angular rate of the Earth's rotation, is the projection of the specific force information output by the NC inertial accelerometer in the navigation coordinate system, is the attitude transformation matrix of NC, is the correlation time of the data link clock frequency error process.

[0046] Let be the radius of the Earth and e be the eccentricity of the Earth's ellipse, and are the radius of curvature along the meridian and the radius of curvature along the prime vertical respectively, and the specific forms are as follows: 、 .

[0047] System noise driving matrix has the specific form of: , where is the attitude transformation matrix of NC. Let the pitch angle, roll angle and heading angle of NC be 、 and respectively, then has the specific form of: .

[0048] has the specific form of , let the white noise of the three-axis measurement of the NC accelerometer be 、 and , the white noise of the three-axis measurement of the gyroscope be 、 and , and the white noise of the clock measurement be , has the specific form of: .

[0049] After that, the continuous form of the state equation of NC is discretized at the given discrete time : , , , where k is the update time.

[0050] In a possible implementation, the navigation state of node i consists of the absolute navigation state and the relative navigation state. Let the system state equation of i be: , where , the subscript i indicates that the item is the navigation state corresponding to node i, the superscript a indicates the absolute navigation state, and r indicates the relative navigation state; , and They are longitude error, latitude error and altitude error respectively; , and They are eastward velocity error, northward velocity error and celestial velocity error respectively; , and They are pitch angle error, roll angle error and heading angle error respectively; and are the local clock error and clock drift respectively, is the state equation of i, is the system noise driving array, is the system noise sequence.

[0051] The specific form is: , F is the state matrix corresponding to the 18-dimensional absolute navigation state, is the conversion matrix from the 18-dimensional absolute navigation state to the 18-dimensional absolute / relative integrated navigation state. The specific form of F is: ,in The establishment method and The same, the difference is that the position, velocity and other parameters in the matrix are provided by the inertial navigation of node i; is the data link clock error process correlation time of the NC modeled in node i; is the state matrix corresponding to the absolute navigation error of the NC modeled in i, and its specific form is: ,in to The specific form is: , , , , , , , , , , , , .

[0052] The relevant parameters with subscript NC are calculated in node i through the position, velocity, attitude and other information broadcast by NC.

[0053] The specific form is: ,in It means taking the first two rows and the first three columns of the matrix. , 、 and The specific form is: 、 、 , 、 are the longitude and latitude of the NC broadcast, 、 are the longitude and latitude of node i itself.

[0054] The specific form of The specific form of G is: , is established in the same way as , the difference is that the attitude information in the matrix is provided by node i; is the system noise driving matrix corresponding to the absolute navigation error of the NC modeled in i, The specific form of , represents the element in the m-th row and n-th column of the attitude transformation matrix , is calculated in i from the attitude information broadcast by the NC, and the calculation method is the same as . Suppose the white noise of the three-axis measurement of the accelerometer of i is 、 and , the white noise of the three-axis measurement of the gyroscope is 、 and , the white noise of the clock measurement is , The specific form of , and then the continuous form of the state equation of i is discretized at the given discrete time to obtain the discrete absolute / relative integrated state equation: , , .

[0055] By establishing the state equations for NC and non-NC nodes respectively, the absolute navigation error and relative navigation error are considered, thus ensuring the stability of the relative navigation information fusion.

[0056] Step S2: Establish a relative navigation information fusion measurement equation according to the input information, obtain the measurement information according to the relative navigation information fusion measurement equation, and ensure the relative spatio-temporal reference under the condition of GNSS denial.

[0057] The relative navigation information fusion measurement equations include the round-trip loop timing measurement equation, the geographical pseudorange measurement equation, and the grid pseudorange measurement equation.

[0058] To ensure the relative space-time reference under the condition of GNSS denial, each node regularly measures the RTT, geographical pseudorange, and grid pseudorange, transmits the measurement data to the central processing unit or distributed processing network, and uses Kalman filtering or other data fusion algorithms to fuse the multi-source measurement data to obtain more accurate relative position and time information. By introducing a clock synchronization mechanism and environmental parameter compensation, the measurement error is further reduced, and the relative position and time reference of each node are updated in real time according to the latest measurement data to ensure the stable operation of the system.

[0059] Round Trip Timing (RTT) refers to the time interval from a node sending a signal to another node and receiving the echo. The round-trip loop timing measurement equation is , where is the clock offset of node i, is the clock offset of NC, is the measurement noise. What is directly measured by RTT is the difference between the data link clock offsets of the sending and receiving members. Assuming that the time reference is borne by NC at the same time, the RTT measurement equation can be obtained as: , where is the clock offset of node i, is the clock offset of NC, is the relative clock offset between node i and NC, is the measurement noise, which follows a Gaussian distribution and satisfies , and the measurement matrix is: .

[0060] Geographical pseudorange refers to the estimated value of the distance between nodes calculated based on geographical locations (longitude, latitude, altitude), that is, what is measured by geographical pseudorange is the distance between nodes i and j in the geographical coordinate system. The geographical pseudorange measurement equation is:

[0061] , where , , are the estimated values of the longitude, latitude, and altitude of node i respectively, , , are the estimated values of the longitude, latitude, and altitude of node j respectively, , , are the errors of the longitude, latitude, and altitude of node i respectively, , , are the estimated values of the errors of the longitude, latitude, and altitude of node i; , , The inertial navigation position error estimated for node j 、 、 The inertial navigation position error estimated for node j The clock error of i relative to NC The clock error of j relative to NC The random measurement noise The measurement noise of the geographical pseudorange population The first parameter

[0062] The first parameter 、matrix 、 、 、 The specific forms of are as follows: where: 、 、 and 、 、 Are the estimated position values of i and j in the earth coordinate system 、 、 、 where , ,The geographical pseudorange system measurement matrix is: 。

[0063] When the position quality of the source obtains the measurement noise of the geographical pseudorange population, the equivalent measurement noise covariance of the geographical pseudorange measurement is: where Represents the absolute horizontal position error covariance matrix of node j; Represents the absolute height error covariance of node j; Represents the relative clock phase error covariance of node j; Represents the geographical pseudorange measurement noise The corresponding covariance. 、 、 Are all calculated according to the error quality transmitted in the PPLI message.

[0064] Suppose that in the PPLI message sent from node j to node i, the absolute horizontal position quality, height quality and time quality of node j are respectively 、 and 。Then according to the conversion table of quality level and position error correspondence, The corresponding uncertainty range is ( , ), The corresponding uncertainty range is ( , ). The corresponding uncertainty range is ( , ). Take the upper limit values for , and for assignment: , , , and we get: .

[0065] The grid pseudorange is an estimated value of the distance between nodes calculated based on a specific coordinate system (such as the UTM coordinate system). The grid pseudorange measurement equation is:

[0066] ;

[0067] ;

[0068] , where , , are the grid position errors of node i; , , are the estimated values of the grid position errors of node i; , , are the grid position errors of node j; , , are the estimated values of the grid position errors of node j; is the random measurement noise, is the first matrix.

[0069] The matrix M is in the same form as the M matrix in the state equation. , the first matrix has the specific form: , , and the grid pseudorange system measurement matrix is: .

[0070] When obtaining the measurement noise of the overall grid pseudorange from the mass of the source, the equivalent measurement noise covariance of the grid pseudorange measurement is: , where represents the covariance matrix of the relative horizontal position error of node j; represents the covariance of the absolute height error of node j; represents the covariance of the relative clock phase error of node j; Indicates the grid pseudorange measurement noise The corresponding covariance 、 、 Are all calculated according to the error quality transmitted in the PPLI message

[0071] In the PPLI message sent from node j to node i, let the relative horizontal position quality, altitude quality, and time quality of node j be 、 And respectively. Then, according to the conversion table of quality level corresponding to position error, it can be known that The corresponding uncertainty range is ( , ), The corresponding uncertainty range is ( , ), The corresponding uncertainty range is ( , ). The upper limit values are taken for 、 And for assignment, obtaining 、 、 , and it can be obtained that .

[0072] Step S3: Based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, select the corresponding Kalman filter according to the measurement information to perform relative navigation information fusion to obtain the navigation result

[0073] In order to perform relative navigation information fusion through the Kalman filter based on the relative navigation information fusion state equation and the measurement equation and obtain the navigation result, it is first necessary to establish a system model. The state vector of each node includes parameters such as position, velocity, and clock bias, and the state equation describes the evolution of these states over time. At the same time, the measurement equation describes the relationship between the observed values and the system state, usually involving non-linear functions such as round-trip timing (RTT), geographical pseudorange, and grid pseudorange. According to the linear or non-linear characteristics of the system, different Kalman filters can be selected, such as the linear Kalman filter (LKF), the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), to predict and update the state estimate and covariance

[0074] The Kalman filter will provide the optimal state estimation for each node, including position, velocity, and clock bias. This information can be used for relative navigation to ensure that the relative spatio-temporal reference between nodes remains accurate and reliable under GNSS-denied conditions. By selecting appropriate state equations, measurement equations, and the Kalman filter, the fusion of relative navigation information can be effectively achieved, improving the accuracy and reliability of the navigation system.

[0075] The input information includes the results of the onboard inertial navigation solution, PPLI messages, pseudorange information, and RTT relative clock offset. The results of the onboard inertial navigation solution include position information, velocity information, and attitude information. The PPLI messages include the absolute navigation information of other aircraft, relative navigation information, absolute navigation quality, and relative navigation quality information. The navigation results include absolute position information, absolute position quality, relative position information, and relative position quality. The absolute position information includes the inertial navigation longitude, latitude, and altitude information corrected by the relative navigation information. The absolute position quality information is calculated from the filtering covariance matrix in the relative navigation information fusion. The relative position information includes U, V, W coordinate information. The relative position quality information is calculated from the filtering covariance matrix in the relative navigation information fusion.

[0076] Please refer to Figure 2 , Figure 2 FIG. shows the schematic diagram of the input information and navigation results proposed in the embodiments of the present application. The relative navigation source selection information fusion receives four main types of input information: the results of the onboard inertial navigation solution, PPLI messages, pseudorange information, and RTT relative clock offset. The results of the onboard inertial navigation solution are provided by the avionics inertial unit and include position (longitude, latitude, altitude), velocity (eastward, northward, upward velocity), and attitude (pitch angle, roll angle, heading angle) information. The PPLI messages are sent by the sending aircraft and contain the absolute navigation information of other aircraft, relative navigation information, absolute navigation quality, and relative navigation quality information. The pseudorange information is calculated through signal detection and represents the distance between two aircraft; while the RTT relative clock offset reflects the time deviation between the local machine and the time reference. The output information mainly includes absolute position information and relative position information and their corresponding quality evaluations. The absolute position information is the inertial navigation longitude, latitude, and altitude corrected by the relative navigation information, ensuring more accurate absolute positioning. The absolute position quality information is calculated from the filtering covariance matrix and is used to evaluate the absolute positioning accuracy. The relative position information represents the relative position of the local machine in the coordinate system constructed at the network center in terms of U, V, W coordinates. Its quality information is also calculated from the filtering covariance matrix to ensure the reliability of relative positioning. Through these processes, the information fusion module significantly improves the accuracy and reliability of the navigation system.

[0077] Figure 3FIG. 0 shows a schematic diagram of the filters adopted for relative navigation information fusion for different measurement information and the state variables that should be corrected, which shows the relationship between different measurement types, filter types, and the state variables they correct. Specifically, for RTT (round-trip time) measurement, Kalman filtering is used to correct the relative clock; for geographic pseudorange measurement, extended Kalman filtering is used to correct the absolute navigation error and the relative clock; while for grid pseudorange measurement, extended Kalman filtering is also used, but mainly corrects the relative navigation error and the relative clock.

[0078] Step S4: After completing the state estimation of one round of relative navigation information fusion, correct the navigation error state according to the measurement information, and report the corrected relative positioning result to the airborne avionics system.

[0079] After completing the state estimation of one round of relative navigation information fusion, the system corrects the navigation error state according to the measurement information. Specifically, by fusing the results of the local inertial navigation solution, PPLI messages, pseudorange information, and RTT relative clock differences, more accurate absolute and relative position information and their quality assessments are calculated. Subsequently, the corrected relative positioning result is reported to the airborne avionics system to ensure that the accuracy and reliability of the navigation system are further improved. Not only the navigation error is corrected, but also more accurate basic data is provided for subsequent navigation operations, thus ensuring the safety and efficiency of the flight mission.

[0080] When establishing the measurement equation for relative navigation information fusion, the source quality information in the message transmitted by the data link is combined, and the source quality information is converted into measurement noise.

[0081] When establishing the measurement equation for relative navigation information fusion, the system combines the source quality information in the message transmitted by the data link. The absolute navigation quality information and relative navigation quality information contained in the PPLI message are used to adjust the measurement model. These quality information reflect the reliability and accuracy of the navigation data. By converting the source quality information into the form of a measurement noise covariance matrix, the statistical characteristics of the measurement error can be more accurately described. Ensure that the measurement equation can more accurately reflect the uncertainty of the actual measurement, thereby improving the accuracy of state estimation. Finally, the corrected relative positioning result is reported to the airborne avionics system to ensure the reliability and accuracy of the navigation system.

[0082] The functional software formed by the relative navigation information fusion method will reside at the data link terminal in the form of a software configuration item.

[0083] A software configuration item is a software module that has been developed, tested, and verified. It has a clear function definition and interface specification. These configuration items can be easily version-controlled, updated, and maintained. Residing on the data link terminal means that this software module will be installed and run on the data link terminal, ensuring that it can handle the fusion task of relative navigation information in real time during data link communication. The data link terminal is a key device responsible for receiving, processing, and transmitting navigation-related information. Deploying it here can ensure the timeliness and accuracy of information.

[0084] Take the position of the sea level corresponding to the true absolute position of the navigation controller as the origin of the grid coordinate system.

[0085] Rotate the geographic coordinate system where the origin is located by an angle θ around the celestial axis. The new three-axis directions obtained will be used as the U, V, and W axes of the grid coordinate system. The angle θ is the heading angle error output by the inertial navigation of the navigation controller.

[0086] After rotating by the angle θ around the celestial axis, the V axis of the grid coordinate system deviates from the local north direction, and the U axis deviates from the local east direction, so that the true value of the heading of the navigation controller in the grid coordinate system is equal to the erroneous heading indicated by the inertial navigation of the navigation controller.

[0087] The relative positions of all members are characterized in the relative coordinate system established by the NC. The establishment method of the relative coordinate system is as follows: At the moment of NC broadcast, take the position of the sea level corresponding to the true absolute position of the NC as the origin of the grid coordinate system. Rotate the geographic system where the origin is located by an angle, and the three-axis directions obtained will be used as the UVW axes of the grid coordinate system. Among them, the angle defined for rotation around the celestial axis is the heading angle error output by the inertial navigation of the NC. After rotating by the angle around the celestial axis, the V axis of the grid coordinate system slightly deviates from the local north direction, and the U axis slightly deviates from the local east direction, so that the true value of the heading of the NC in the grid coordinate system is the erroneous heading indicated by the inertial navigation of the NC. Under this coordinate system establishment method, with the elevation constraint of the barometric altimeter, the grid coordinates of the NC can be accurately (0, 0, h).

[0088] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0089] First, the implementation cost is low and the versatility is good. The relative navigation function software formed by the relative navigation source information fusion method designed by the present invention is realized by loading software configuration items in the data link terminal. It receives PPLI messages, pseudorange, RTT measurement values, and local inertial navigation data through the reserved function module interface, executes the module function, and there is no need to modify the hardware of the existing data link terminal, so it has strong versatility and greatly reduces the implementation cost.

[0090] Second, the relative navigation information fusion algorithm is theoretically complete, and the fusion result is stable and reliable. The designed relative navigation information fusion method fully considers the source navigation error, solves the estimation stability problem caused by the existing information fusion method ignoring the source navigation error, and also suppresses the overconfidence problem brought by distributed information fusion.

[0091] Third, it has strong access adaptability. The solution designs the scheme based on the existing data link, and can adapt to the existing data link without modifying the data link message protocol and member role assignment, which is a deep excavation and upgrade of the navigation ability of the existing data link.

[0092] Fourth, the scheme runs with good real-time performance, meeting the requirements of high-dynamic navigation data update. The dimensionality of the relative navigation information fusion state modeling has requirements for computing power and storage resources within the capabilities of the existing platform hardware processing resources, and there is a large margin, meeting the real-time requirements of the algorithm under high-dynamic conditions.

[0093] Fifth, it is applicable to the realization of relative navigation technology based on data link under the condition of GNSS denial to construct a local spatio-temporal reference centered on NC, serving the relative positioning between aircraft within the cluster. The designed relative navigation information fusion method is implemented through software algorithms. The designed method has strong versatility and portability, is compatible with the message protocols and member role assignments of active data links, has low implementation cost, ensures the theoretical completeness of the information fusion method, improves the navigation ability of the existing data link, and has strong engineering application prospects.

[0094] The following gives a possible implementation manner of a data link relative navigation information fusion device, which is used to execute each execution step and corresponding technical effects of the data link relative navigation information fusion method shown in the above embodiments and possible implementation manners. The device includes:

[0095] A state equation establishment module, configured to establish a relative navigation information fusion state equation according to the input information, including an absolute navigation error state equation for the navigation controller, an absolute navigation error state equation for non-navigation controllers, and a relative navigation error state equation;

[0096] A measurement equation establishment module, configured to establish a relative navigation information fusion measurement equation according to the input information to ensure the relative spatio-temporal reference under the condition of GNSS denial. The relative navigation information fusion measurement equation includes a round-trip loop timing measurement equation, a geographical pseudorange measurement equation, and a grid pseudorange measurement equation;

[0097] A navigation result generation module, configured to perform relative navigation information fusion based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, and select a corresponding Kalman filter according to the measurement information to obtain a navigation result;

[0098] An error correction module is used to correct the navigation error state according to the measurement information after the state estimation of one round of relative navigation information fusion, and report the corrected relative positioning result to the airborne avionics system.

[0099] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A data link relative navigation information fusion method, characterized in that: The method comprises: Establishing a relative navigation information fusion state equation according to the input information, including an absolute navigation error state equation for a navigation controller, an absolute navigation error state equation for a non-navigation controller, and a relative navigation error state equation; Establish a relative navigation information fusion measurement equation based on the input information, and obtain measurement information based on the relative navigation information fusion measurement equation to ensure the relative time and space reference under the satellite navigation denial condition. The relative navigation information fusion measurement equation includes the round-trip timing measurement equation, the geographic pseudo-range measurement equation and the grid pseudo-range measurement equation; Based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, the corresponding Kalman filter is selected according to the measurement information to perform relative navigation information fusion to obtain the navigation result; After completing a round of state estimation of relative navigation information fusion, the navigation error state is corrected according to the measurement information, and the corrected relative positioning result is reported to the airborne avionics system.

2. The data link relative navigation information fusion method according to claim 1, characterized in that: The absolute navigation error state equation of the navigation controller is: ,in is the time derivative of the NC system state vector, is the state equation of NC in the absolute navigation state, is the NC system state vector, is the system noise driving array, is the system noise sequence.

3. The data link relative navigation information fusion method according to claim 1, characterized in that: The round trip timing measurement equation is: ,in is the clock difference of node i, is the clock error of NC, To measure noise; The geographic pseudorange measurement equation is: ,in , , are respectively the high estimates of latitude and longitude of node i, , , are the latitude and longitude errors of node i, , , are the estimated values ​​of the latitude and longitude errors of node i, is the clock error of i relative to NC, is the measurement noise of the geographic pseudorange population, is the first parameter; The grid pseudorange measurement equation is: ,in , , is the grid position error of node i; , , is the estimated value of the grid position error of node i; , , is the grid position error of node j; , , is the estimated value of the grid position error of node j; is the random measurement noise, is the first matrix.

4. The data link relative navigation information fusion method according to claim 1, characterized in that: When establishing the relative navigation information fusion measurement equation, the source quality information in the message transmitted by the data link is combined to convert the source quality information into measurement noise.

5. The data link relative navigation information fusion method according to claim 1, characterized in that: The functional software formed by the relative navigation information fusion method will reside in the data link terminal in the form of software configuration items.

6. The data link relative navigation information fusion method according to claim 1, characterized in that: The input information includes the local inertial navigation solution result, PPLI message, pseudorange information and RTT relative clock error. The local inertial navigation solution result includes position information, speed information and attitude information. The PPLI message includes the absolute navigation information, relative navigation information, absolute navigation quality and relative navigation quality information of the other machine.

7. The data link relative navigation information fusion method according to claim 1, characterized in that: The navigation results include absolute position information, absolute position quality, relative position information and relative position quality. The absolute position information includes the inertial navigation longitude, latitude and altitude information corrected by the relative navigation information. The absolute position quality information is calculated by the filter covariance matrix in the fusion of relative navigation information. The relative position information includes U, V and W coordinate information. The relative position quality information is calculated by the filter covariance matrix in the fusion of relative navigation information.

8. The data link relative navigation information fusion method according to claim 1, characterized in that: The sea level position corresponding to the real absolute position of the navigation controller is used as the origin of the grid coordinate system; The geographic coordinate system where the origin is located is rotated by an angle θ around the celestial axis, and the new three-axis directions are obtained as the U, V, and W axes of the grid coordinate system. The angle θ is the heading angle error output by the inertial navigation of the navigation controller; After rotating around the celestial axis by an angle θ, the V axis of the grid coordinate system deviates from the local north direction, and the U axis deviates from the local east direction, so that the true value of the navigation controller's heading in the grid coordinate system is equal to the erroneous heading indicated by the navigation controller's inertial navigation.

9. A data link relative navigation information fusion device, characterized in that: The device comprises: A state equation building module, used for building a relative navigation information fusion state equation according to input information, including an absolute navigation error state equation for a navigation controller, an absolute navigation error state equation for a non-navigation controller, and a relative navigation error state equation; The measurement equation establishment module is used to establish the relative navigation information fusion measurement equation according to the input information to ensure the relative time and space reference under the satellite navigation denial condition. The relative navigation information fusion measurement equation includes the round-trip timing measurement equation, the geographic pseudo-range measurement equation and the grid pseudo-range measurement equation; A navigation result generation module is used to select a corresponding Kalman filter to perform relative navigation information fusion based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation according to the measurement information to obtain a navigation result; The error correction module is used to correct the navigation error state according to the measurement information after completing a round of state estimation of relative navigation information fusion, and report the corrected relative positioning result to the airborne avionics system.

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