A method, device, equipment and storage medium for airborne navigation filtering

Abstract

The present invention provides an airborne navigation filtering method, device, computer equipment and storage medium, belonging to the field of airborne navigation design, the method comprising: initializing the filtering parameters of the airborne navigation system of an unmanned aerial vehicle; positioning the unmanned aerial vehicle according to satellite navigation signals, and determining the navigation parameters of the unmanned aerial vehicle at the current moment; determining the navigation filtering residual and residual variance of the unmanned aerial vehicle; updating the initialized filtering factor according to the navigation filtering residual and the preset residual threshold, and the residual variance and the preset residual variance threshold, respectively, to obtain the updated filtering factor; filtering and optimizing the airborne navigation system according to the updated filtering factor. The method has strong environmental adaptability, can generate filtering factors corresponding to the current environment for airborne navigation solutions under different vibration magnitude environments, improves the accuracy of filtering the airborne navigation system, and has strong engineering application value.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle control system design, and in particular relates to an airborne navigation filtering method, device, equipment and storage medium. Background Art

[0002] With the rapid development of unmanned system technology, unmanned aerial vehicles will face more and more work tasks and will inevitably face complex vibration working environments. So far, airborne navigation systems mostly use a combined navigation system of inertial navigation / satellite. In a complex vibration environment, such as flight impact and flight vibration, the performance of inertial navigation devices will gradually change. The unknown changes in the performance indicators of such inertial devices not only affect the initial parameter design of the combined navigation filter system, but also bring large deviations to the navigation solution. Therefore, how to optimize the filter design so that the navigation system can achieve high-precision solution in a complex vibration environment has become a current difficulty.

[0003] In a complex vibration environment, the zero position and stability index of the inertial navigation system will change to a certain extent. To address this phenomenon, the commonly used methods are from the two aspects of initial filter parameter design and physical protection. When designing the initial filter parameters, a complex vibration environment is simulated on the ground to identify the vibration characteristic parameters of the inertial navigation system, so as to provide a corresponding reference for the initial parameter design of the combined navigation, and to achieve the adaptability of the inertial navigation / satellite combined navigation system to the complex vibration environment by adjusting the initial filter parameters. However, this method requires a ground simulation of the vibration parameters of each navigation system, and the operation procedure is complex and the engineering application capability is weak. When optimizing the design through the physical structure, the buffer structure needs to be designed according to the performance index requirements to achieve physical isolation of vibration interference and improve the anti-interference ability of the navigation system. However, the defect of this method is that it not only increases the difficulty of the overall structural design of the navigation system, but also increases its mass and volume, and greatly increases the cost of the navigation system.

[0004] Therefore, the existing technology still lacks a practical solution to the problem of poor stability of the inertial navigation system in a complex vibration environment. Summary of the invention

[0005] In order to solve the problem that the state information of an unmanned aerial vehicle cannot meet the stability requirement, the present invention provides an airborne navigation filtering method, device, equipment and storage medium.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] First, an airborne navigation filtering method is provided, the method comprising:

[0008] Acquire initial navigation parameters of the unmanned aerial vehicle airborne navigation system; the initial navigation parameters include initial filter factors and initial state information;

[0009] Perform inertial navigation calculation on the UAV and determine the actual state information of the UAV in combination with the initial state information;

[0010] Performing adaptive navigation filtering on the actual state information according to the satellite navigation information to determine a navigation filtering residual matrix and a residual variance matrix of the unmanned aerial vehicle state information;

[0011] The initial filter factor is updated according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset variance threshold, respectively, to obtain an updated filter factor;

[0012] The actual state information is filtered and optimized according to the updated filter factor.

[0013] Optionally, the obtaining of initial navigation parameters of an onboard navigation system of the unmanned aerial vehicle comprises:

[0014] The navigation parameters of the unmanned aerial vehicle airborne navigation system are initialized based on the inertial navigation / satellite adaptive extended Kalman filter algorithm under loose coupling conditions to obtain initial navigation parameters.

[0015] Optionally, the actual state information includes actual motion information and actual position information; performing inertial navigation solution on the unmanned aerial vehicle and determining the actual state information of the unmanned aerial vehicle in combination with the initial state information includes:

[0016] Perform inertial navigation calculation based on the angular velocity and acceleration of the UAV to determine the actual motion information of the UAV;

[0017] The actual position information of the unmanned aerial vehicle is determined according to the actual motion information and the initial state information.

[0018] Optionally, the performing adaptive navigation filtering on the actual state information according to the satellite navigation information to determine the navigation filtering residual matrix and residual variance matrix of the unmanned aerial vehicle state information includes:

[0019] Establishing an adaptive navigation filtering model based on the actual state information;

[0020] Predicting the predicted state information of the unmanned aerial vehicle at the current moment according to the adaptive navigation filtering model;

[0021] The navigation filter residual matrix and the residual variance matrix are determined according to the predicted state parameters and the actual state information at the current moment.

[0022] Optionally, the updating of the initial filter factor according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset variance threshold, to obtain the updated filter factor comprises:

[0023] When the navigation filter residual matrix does not meet the preset requirement, that is, when the navigation filter residual matrix ε K When the position item in is greater than the first preset residual threshold, the filter factor K is adjusted F =K F +0.001; or, in the navigation filter residual matrix ε K When the speed term in is greater than the second preset residual threshold, the filter factor K is adjusted F =K F -0.001;

[0024] When it is determined that the navigation filter residual matrix meets the preset requirements, and the residual variance matrix does not meet the preset requirements, that is, when the residual variance matrix C K When the position item in is greater than the first preset variance threshold, the filter factor is adjusted to K F =K F +0.0001; or, the residual variance matrix C K When the speed term in is greater than the second preset variance threshold, the filter factor is adjusted to K F =K F -0.0001;

[0025] The adaptive navigation filter model is iterated according to the adjusted filter factor to obtain an updated filter factor.

[0026] Optionally, iterating the adaptive navigation filtering model according to the adjusted filtering factor to obtain an updated filtering factor includes:

[0027] For the filter factor K F Perform output clipping within the range of the value range, when both the navigation filter residual matrix and the residual variance matrix meet the preset requirements, or K F When the value range limit is reached, the iteration ends;

[0028] The last adjusted filter factor is determined as the updated filter factor.

[0029] Secondly, an airborne navigation filtering device is also provided, the device comprising:

[0030] An initialization module, used to initialize the filtering parameters of the unmanned aerial vehicle airborne navigation system; the filtering parameters include filtering factors and navigation parameters;

[0031] A determination module is used to locate the unmanned aerial vehicle according to the satellite navigation signal and determine the navigation parameters of the unmanned aerial vehicle at the current moment; according to the initialized filter parameters and the navigation parameters at the current moment, determine the navigation filter residual matrix and the residual variance matrix of the unmanned aerial vehicle;

[0032] An updating module, used to update the initialized filter factors according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset residual variance threshold, respectively, to obtain updated filter factors;

[0033] The optimization module is used to perform filtering optimization on the actual state information according to the updated filtering factor.

[0034] In addition, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned airborne navigation filtering method is implemented.

[0035] Finally, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned airborne navigation filtering method when executing the program.

[0036] The airborne navigation filtering method provided by the present invention has the following beneficial effects:

[0037] First, the inertial navigation solution of the unmanned aerial vehicle is performed, and then the solution result is filtered through the satellite navigation information, so that the unmanned aerial vehicle state information can be optimized through the satellite navigation information, and there is no need to make structural optimization changes to the inertial navigation components, which greatly saves the design cost of the product; secondly, in the process of optimizing the state information of the unmanned aerial vehicle through satellite navigation information, the adjustment of the filter factor is achieved through the navigation filter residual and the residual variance, which can not only further reduce noise interference, but also realize real-time updating of the filter factor. Therefore, the requirements for the initial navigation parameters are low, and there is no need to survey the vibration parameters of the navigation system. In addition, the environmental adaptability is high, and the airborne navigation solution under different vibration magnitude environments can generate the filter factor corresponding to the current environment, which is beneficial to solving the stability problem of the state information of the unmanned aerial vehicle and has a strong engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1The present invention is a flowchart of an airborne navigation filtering method provided according to an exemplary embodiment of the present invention.

[0040] Figure 2 The present invention is a flow chart of an airborne navigation filtering method provided according to another exemplary embodiment of the present invention.

[0041] Figure 3 The present invention is a block diagram of an airborne navigation filtering device according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0043] The present invention proposes a navigation adaptive filtering method applied to a complex vibration environment. By solving the residual and the residual variance value in the inertial navigation / satellite solution process, an adaptive filtering training model is established according to the speed and position residuals and the residual variance values ​​of the combined navigation real-time solution and the satellite navigation, and the adaptive filtering factors in the navigation filtering algorithm are accurately calculated in real time, so as to realize the high-precision solution of the airborne combined navigation system and ensure that the navigation system meets the high performance index requirements in a complex vibration environment.

[0044] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] First, the present invention provides an airborne navigation filtering method, specifically, Figure 1 As shown, the following steps are included:

[0046] S101, obtaining initial navigation parameters of the onboard navigation system of the unmanned aerial vehicle.

[0047] Firstly, the navigation parameters of the unmanned aerial vehicle are initialized to obtain the initial navigation parameters of the unmanned aerial vehicle, wherein the initial navigation parameters include an initial filtering factor and initial state information.

[0048] Specifically, the navigation parameters of the unmanned aerial vehicle airborne navigation system are initialized based on the inertial navigation / satellite adaptive extended Kalman filter algorithm under loose coupling conditions to obtain initial navigation parameters.

[0049] In this step, when initializing the filtering parameters of the unmanned aerial vehicle navigation system, it is determined that the navigation filtering algorithm used in the design of the airborne navigation system is an inertial navigation / satellite adaptive extended Kalman filtering algorithm based on loosely coupled conditions. Based on the design principle of the navigation algorithm, the initialization process of all navigation parameters needs to be completed, including the following parameter results:

[0050] Navigation initial position information: L (0) ,λ (0) ,H (0) ; Navigation initial speed information: Navigation initial attitude information: θ (0) ,ψ (0) ,γ (0) ; Navigation filter initial state matrix: Navigation filter initial variance matrix: Navigation filter parameter array: Navigation filter parameter array: R = diag (0.0000002, 0.0000002, 8, 0.1, 0.1, 0.1); Navigation filter adaptive factor: K F =1.

[0051] S102: Perform inertial navigation calculation on the UAV, and determine the actual state information of the UAV in combination with the initial state information.

[0052] The actual state information includes actual motion information and actual position information.

[0053] In this step, according to the mission objectives of the UAV, if it will work in a complex vibration environment, the adaptive filtering mode can be selected; after entering the filtering mode, the inertial navigation solution is firstly performed based on the angular velocity and acceleration of the UAV to determine the actual motion information of the UAV; secondly, the actual position information of the UAV is determined based on the actual motion information and the initial state information. .

[0054] For example, the inertial navigation solution is first performed, and the specific process is as follows:

[0055] The inertial navigation solution part uses the angular velocity and acceleration information of the inertial navigation system as input information to solve the navigation information. The main calculation method is as follows:

[0056] According to the angular velocity information output by the inertial navigation device, the inertial solution attitude update equation is performed:

[0057]

[0058] Where: Δt SINS is the step length of inertial navigation solution; θ (m-1) and θ (m) t m-1 and t m The airborne navigation pitch angle at time ψ (m-1) and ψ (m) t m-1 and t m The airborne navigation heading angle at the moment; γ (m-1) and γ(m) t m-1 and t m The onboard navigation roll angle at the moment; and t m The inertial navigation output angular velocity information at the moment.

[0059] The inertial navigation velocity update equation is also called the specific force equation. It refers to the basic equation for inertial navigation solution near the surface of the earth. It is as follows:

[0060]

[0061] in: t m Three-direction speed information under the real-time navigation system; t m-1 Three-direction speed information under the real-time navigation system; t m The relative force information output by the sensor is added to the time inertial navigation; t m-1 Gravity acceleration vector information in the current navigation system; t m-1 The earth's rotation information in the real-time navigation system, ω ie The constant angular velocity of the Earth's rotation is 15° / h, L (m-1) t m-1 The geographical latitude at the time.

[0062] t m-1 Angular velocity information from the earth coordinate system to the navigation system at the moment, R M(m-1) t m-1 The meridian radius under the earth parameter model at the moment, R N(m-1) t m-1 The radius of the Maoyou circle under the earth parameter model at this moment.

[0063] In the position update equation of the inertial navigation, the real-time geographic latitude, geographic longitude and altitude information are solved and output in real time, as shown in the following formula:

[0064]

[0065] Where: L (m) ,λ (m) , H (m) t m The geographical latitude, longitude and altitude under the current navigation system; L (m-1) ,λ (m-1) , H (m-1) t m-1 The geographical latitude, longitude and altitude under the current navigation system.

[0066] S103: Perform adaptive navigation filtering on the actual state information according to the satellite navigation information to determine a navigation filtering residual matrix and a residual variance matrix of the UAV state information.

[0067] Specifically, an adaptive navigation filter model is established based on the actual state information; the predicted state information of the unmanned aerial vehicle at the current moment is predicted according to the adaptive navigation filter model; and the navigation filter residual matrix and the residual variance matrix are determined according to the predicted state parameters and the actual state information at the current moment.

[0068] In this step, it is necessary to design the filter of the airborne navigation system. The design content is to build an adaptive navigation filter algorithm based on inertial navigation solution and optimize the adaptive navigation filter model through iteration.

[0069] The purpose of the adaptive navigation filter algorithm is to use the data of the effective time of satellite positioning to correct the navigation results when the inertial navigation system has a large deviation in its inertial navigation solution due to various complex working conditions. The construction of the adaptive navigation filter model includes defining the following parameters:

[0070] Determine the filter factor K F .

[0071] State one-step prediction equation:

[0072] Navigation filter residual calculation equation:

[0073] State estimation equation:

[0074] Filter residual variance matrix:

[0075] Filter gain calculation equation:

[0076] One-step forecast mean square error equation:

[0077] Mean square error filter estimation equation:

[0078] Among them, the state variable X K The variable contents are: δL, δλ, δH, δV E ,δV N ,δV U ,δθ,δψ,δγ.

[0079] The following formula shows the error transmission mode of inertial navigation. The filtering state equation can be established according to the following formula: k,k-1 =I-φ*ΔtSINS .

[0080] in:

[0081]

[0082]

[0083] Measurement information Z K The variable information is: [L,λ,H,V E ,V N ,V U ].

[0084] The measurement transfer equation is as follows: H K =diag(1,1,1,1,1,1).

[0085] Q and R are filtering parameters.

[0086] In some embodiments of the present invention, the assignment of filtering parameters in the above steps mainly includes parameters P0, Q, and R. The setting of filtering parameter P0 is completed according to the accuracy of the initial navigation information, the setting of filtering parameter R is completed according to the positioning accuracy of the satellite navigation system, and the setting of filtering parameter Q is completed according to the performance indicators of the inertial device.

[0087] S104: Update the initial filter factor according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset variance threshold, respectively, to obtain an updated filter factor.

[0088] Specifically, when the navigation filter residual matrix does not meet the preset requirement, that is, when the navigation filter residual matrix ε K When the position item in is greater than the first preset residual threshold, the filter factor K is adjusted F =K F +0.001; or, in the navigation filter residual matrix ε K When the speed term in is greater than the second preset residual threshold, the filter factor K is adjusted F =K F -0.001. When it is determined that the navigation filter residual matrix meets the preset requirements, and the residual variance matrix does not meet the preset requirements, that is, when the residual variance matrix C K When the position item in is greater than the first preset variance threshold, the filter factor is adjusted to K F =K F +0.0001; or, the residual variance matrix C K When the speed term in is greater than the second preset variance threshold, the filter factor is adjusted to K F =K F-0.0001; iterate the adaptive navigation filter model according to the adjusted filter factor to obtain an updated filter factor.

[0089] For example, the filter factor K F Perform output clipping within the range of the value range, in which the navigation filter residual matrix and the residual variance matrix meet the preset requirements, or K F When the value range limit is reached, the iteration is terminated; and the last adjusted filter factor is determined as the updated filter factor.

[0090] For example, the first preset residual threshold may be 20, the second preset residual threshold may be 0.2, the first preset variance threshold may be 500, and the second preset variance threshold may be 3. That is, if the navigation filter residual matrix ε K If the position item in is greater than 20, adjust the filter factor K F =K F +0.001; if the navigation filter residual matrix ε K If the speed term in is greater than 0.2, adjust the filter factor K F =K F -0.001; If the navigation filter residual matrix is ​​determined to meet the requirements, the residual variance matrix is ​​judged. If the residual variance matrix C K If the position item in is greater than 500, adjust the filter factor to K F =K F +0.0001; if the residual variance matrix C K If the speed term in is greater than 3, adjust the filter factor to K F =K F -0.0001. For the filter factor K F The output is limited to the value range [1, 1.01].

[0091] S105: Filter and optimize the actual state information according to the updated filter factor.

[0092] The updated filter factor is used to construct an adaptive navigation filter model. According to the navigation solution cycle, the navigation solution filtering work under the real-time conditions of the unmanned aerial vehicle is continuously performed until the navigation mission is completed.

[0093] By adopting the above method, the inertial navigation solution of the unmanned aerial vehicle is firstly performed, and then the solution result is filtered through the satellite navigation information, so that the optimization of the state information of the unmanned aerial vehicle can be realized through the satellite navigation information, and there is no need to make structural optimization changes to the inertial navigation components, which greatly saves the design cost of the product; secondly, in the process of optimizing the state information of the unmanned aerial vehicle through the satellite navigation information, the adjustment of the filter factor is realized through the navigation filter residual and the residual variance, which can not only further reduce the noise interference, but also realize the real-time update of the filter factor, so the requirements for the initial navigation parameters are low, and there is no need to survey the vibration parameters of the navigation system, and the environmental adaptability is high, and the airborne navigation solution under different vibration magnitude environments can generate the filter factor corresponding to the current environment, which is conducive to solving the stability problem of the state information of the unmanned aerial vehicle and has a strong engineering application value.

[0094] In addition, based on the above method, Figure 2 As shown, the present application also provides an achievable implementation method, and the specific steps are as follows:

[0095] Step S1: The unmanned aerial vehicle navigation system initializes navigation filter parameters, including initial navigation parameters and filter initial parameters before takeoff.

[0096] Step S2: According to the working objectives of the UAV, if it will work in a complex vibration environment, the adaptive filtering mode is selected.

[0097] Step S3: The navigation system completes the process of solving the navigation filter residual and the residual variance matrix according to the positioning status of the satellite navigation.

[0098] Step S4: According to the initially set residual and residual variance value threshold, the navigation filter solution result is judged against the set threshold. If it exceeds the threshold, the adaptive filter factor value is updated according to the deviation between the navigation filter solution result and the threshold.

[0099] Step S5: According to the combined navigation filtering data of the previous frame, the adaptive filtering parameters are updated and then the navigation system filtering solution is performed. At the same time, the residual of the filtering result and the residual variance are compared with the set threshold. If the set requirements are met, the updating of the adaptive filtering factor is terminated. If the requirements are not met, the filtering factor is updated repeatedly and proceeds to the Hagen solution.

[0100] Step S6: According to the navigation solution cycle, the navigation solution work of the unmanned aerial vehicle under real-time conditions is continuously performed until the navigation mission is completed.

[0101] Secondly, the present invention also provides an airborne navigation filtering device, such as Figure 3 As shown, including:

[0102] Initialization module 301, used to initialize the filtering parameters of the UAV airborne navigation system; the filtering parameters include filtering factors and navigation parameters;

[0103] The determination module 302 is used to locate the UAV according to the satellite navigation signal and determine the navigation parameters of the UAV at the current moment; determine the navigation filter residual matrix and residual variance matrix of the UAV according to the initialized filter parameters and the navigation parameters at the current moment;

[0104] An updating module 303 is used to update the initialized filter factors according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset residual variance threshold, respectively, to obtain updated filter factors;

[0105] The optimization module 304 is used to perform filtering optimization on the actual state information according to the updated filtering factor.

[0106] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The steps of the onboard navigation filtering method are provided.

[0107] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The steps of the onboard navigation filtering method are provided.

[0108] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] It should be noted that the above specific implementation method can enable those skilled in the art to understand the invention more comprehensively, but does not limit the invention in any way. Therefore, although the present invention has been described in detail in this specification, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. An airborne navigation filtering method, characterized in that: The method comprises: Acquire initial navigation parameters of the unmanned aerial vehicle airborne navigation system; the initial navigation parameters include initial filter factors and initial state information; Perform inertial navigation calculation on the UAV and determine the actual state information of the UAV in combination with the initial state information; Performing adaptive navigation filtering on the actual state information according to the satellite navigation information to determine a navigation filtering residual matrix and a residual variance matrix of the unmanned aerial vehicle state information; The initial filter factor is updated according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset variance threshold, respectively, to obtain an updated filter factor; The actual state information is filtered and optimized according to the updated filter factor.

2. The airborne navigation filtering method according to claim 1, characterized in that: The obtaining of initial navigation parameters of the unmanned aerial vehicle airborne navigation system comprises: The navigation parameters of the unmanned aerial vehicle airborne navigation system are initialized based on the inertial navigation / satellite adaptive extended Kalman filter algorithm under loose coupling conditions to obtain initial navigation parameters.

3. The airborne navigation filtering method according to claim 1, characterized in that: The actual state information includes actual motion information and actual position information; the inertial navigation solution of the unmanned aerial vehicle and the determination of the actual state information of the unmanned aerial vehicle in combination with the initial state information includes: Perform inertial navigation calculation based on the angular velocity and acceleration of the UAV to determine the actual motion information of the UAV; The actual position information of the unmanned aerial vehicle is determined according to the actual motion information and the initial state information.

4. The airborne navigation filtering method according to claim 1, characterized in that: The method of performing adaptive navigation filtering on the actual state information according to the satellite navigation information to determine the navigation filtering residual matrix and residual variance matrix of the unmanned aerial vehicle state information includes: Establishing an adaptive navigation filtering model based on the actual state information; Predicting the predicted state information of the unmanned aerial vehicle at the current moment according to the adaptive navigation filtering model; The navigation filter residual matrix and the residual variance matrix are determined according to the predicted state parameters and the actual state information at the current moment.

5. The airborne navigation filtering method according to claim 4, characterized in that: The updating of the initial filter factor according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset variance threshold, to obtain the updated filter factor comprises: When the navigation filter residual matrix does not meet the preset requirement, that is, when the navigation filter residual matrix ε K When the position item in is greater than the first preset residual threshold, the filter factor K is adjusted F =K F +0.001; or, in the navigation filter residual matrix ε K When the speed term in is greater than the second preset residual threshold, the filter factor K is adjusted F =K F -0.001; When it is determined that the navigation filter residual matrix meets the preset requirements, and the residual variance matrix does not meet the preset requirements, that is, when the residual variance matrix C K When the position item in is greater than the first preset variance threshold, the filter factor is adjusted to K F =K F +0.0001; or, the residual variance matrix C K When the speed term in is greater than the second preset variance threshold, the filter factor is adjusted to K F =K F -0.0001; The adaptive navigation filter model is iterated according to the adjusted filter factor to obtain an updated filter factor.

6. The airborne navigation filtering method according to claim 5, characterized in that: The iterating of the adaptive navigation filter model according to the adjusted filter factor to obtain the updated filter factor comprises: For the filter factor K F Perform output clipping within the range of the value range, when both the navigation filter residual matrix and the residual variance matrix meet the preset requirements, or K F When the value range limit is reached, the iteration ends; The last adjusted filter factor is determined as the updated filter factor.

7. An airborne navigation filtering device, characterized in that: The device comprises: An initialization module, used to initialize the filtering parameters of the unmanned aerial vehicle airborne navigation system; the filtering parameters include filtering factors and navigation parameters; A determination module is used to locate the unmanned aerial vehicle according to the satellite navigation signal and determine the navigation parameters of the unmanned aerial vehicle at the current moment; according to the initialized filter parameters and the navigation parameters at the current moment, determine the navigation filter residual matrix and residual variance matrix of the unmanned aerial vehicle; An updating module, used to update the initialized filter factors according to the navigation filter residual matrix and the preset residual threshold, and the residual variance matrix and the preset residual variance threshold, respectively, to obtain updated filter factors; The optimization module is used to perform filtering optimization on the actual state information according to the updated filtering factor.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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