Filter parameter reconstruction method, system, device and medium for airborne navigation system

Abstract

The present invention discloses a filter parameter reconstruction method, system, device, and medium for an airborne navigation system, relating to the field of navigation technology. The method comprises the following steps: obtaining combined navigation filter parameters of an extended Kalman filter in the airborne navigation system; aligning an initial attitude angle, obtaining the angular velocity and acceleration of an inertial navigation system at the time of alignment, determining the magnitude of the angular velocity of the inertial navigation system at the time of alignment and a set angular velocity threshold, and removing invalid angular velocities and accelerations of the inertial navigation system at the time of alignment that are greater than the set angular velocity threshold; and performing Allan variance estimation on the remaining angular velocities and accelerations after the invalid angular velocities and accelerations to obtain reset combined navigation filter parameters, and obtaining filter parameter reconstruction results. The present invention eliminates the need for adaptive parameter adjustment during flight, and improves the accuracy of combined navigation filter parameters and the filter convergence of combined navigation by calibrating the initial attitude angle and removing invalid data, thereby significantly reducing the computing power requirements of the missile-borne computer.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a method, system, device and medium for reconstructing filtering parameters of an airborne navigation system. Background Art

[0002] Airborne navigation systems fully consider their autonomy, high dynamic characteristics and all-weather characteristics, and usually use a navigation system that combines MEMS inertial navigation and satellite navigation. The extended Kalman filter (EKF) filtering algorithm is often used in the engineering of the combined navigation filtering algorithm. Due to the complexity of the inertial navigation device and the nonlinear error of the navigation solution, the navigation solution accuracy of the filtering algorithm deviates, which puts higher requirements on the EKF filter parameter matching design.

[0003] In the design of combined navigation filtering parameters for navigation systems that combine airborne inertial navigation and satellite navigation, it is usually adopted to constrain the performance indicators of the inertial navigation during the design phase to ensure the matching consistency between the inertial navigation device and the filtering algorithm. However, due to the structure and process principles of the inertial navigation device, especially for MEMS inertial navigation devices, there will inevitably be deviations in their actual performance indicators, which will have a great impact on the accuracy of the navigation solution.

[0004] Some researchers have achieved continuous correction of the deviation of the initial filter parameter settings by real-time adaptive adjustment of parameters during flight. However, this not only increases the computing power requirements of the missile-borne computer, but also reduces the stability of the filtering algorithm, and its engineering application capabilities are relatively weak. Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and provide a method, system, device and storage medium for reconstructing filtering parameters of an airborne navigation system, so as to solve the problem that the prior art not only increases the requirements for the computing power of the onboard computer, but also reduces the stability of the filtering algorithm and has weak engineering application capabilities.

[0006] The present invention specifically provides the following technical solution: a method for reconstructing filter parameters of an airborne navigation system, comprising the following steps:

[0007] Obtain the combined navigation filter parameters obtained after initialization and assignment in the extended Kalman filter (EKF) of the airborne navigation system; wherein the airborne navigation system is a navigation system combining inertial navigation and satellite navigation;

[0008] Align the initial attitude angle of the airborne navigation system and obtain the angular velocity and acceleration of the alignment inertial navigation. Determine the magnitude of the angular velocity of the alignment inertial navigation relative to the set angular velocity threshold, and remove invalid angular velocities and accelerations when the angular velocity of the alignment inertial navigation exceeds the set angular velocity threshold.

[0009] The Allan variance estimation is performed using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration. The integrated navigation filter parameters obtained after the initialization assignment are reset using the Allan variance estimation results, and the filter parameter reconstruction results are obtained using the reset integrated navigation filter parameters.

[0010] Preferably, the aligning of the initial attitude angle of the airborne navigation system is specifically performed as follows:

[0011] The heading binding alignment method is used to align the initial attitude angle of the UAV with an airborne navigation system; the specific expression is:

[0012]

[0013] The initial attitude angle includes the initial alignment pitch angle θ and the initial alignment roll angle γ; Ax and Ay are the acceleration information in two directions; g is the local gravity acceleration information; Az is the acceleration information on the Z axis of the body; arcsin(·) is the inverse sine function, and arctan(·) is the inverse tangent function.

[0014] Preferably, the Allan variance estimation using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration comprises the following steps:

[0015] Get the sequence of remaining angular velocity and acceleration after removing invalid angular velocity and acceleration; the specific expression is:

[0016]

[0017] Where τ0 is the data update period, N is the sample data point, They are the inertial sensor output data during the alignment of the airborne navigation system, and the inertial sensor output data are angular velocity and acceleration;

[0018] Get the variance of the sampling time during the alignment period as one data update period τ0 The specific expression is:

[0019]

[0020] Continuously double the sampling time to obtain the doubled sampling time τ L , denoted by τ L =2*τ L-1 =2 L *τ0, When the final sequence length is not less than 2, the inertial navigation data sequence is obtained. is the inertial sensor output data during the alignment of the onboard navigation system at the kth sample data point, is the inertial sensor output data during the alignment of the onboard navigation system at the k-1th sample data point; the specific expression is:

[0021]

[0022] Among them, N L is the sample data point after doubling the sampling time, τ L-1 is τ L Half the sampling time, N L-1 N L One-half the number of sample data points;

[0023] Get τ L Variance The specific expression is:

[0024]

[0025] By doubling the sampling time, a series of variance points corresponding to the sampling period are obtained. The gyro angle wander coefficient and the accelerometer velocity wander coefficient are obtained through a series of variance points corresponding to the sampling periods; the specific expressions are:

[0026]

[0027] Among them, N ARW is the gyro angle wander coefficient or the accelerometer velocity wander coefficient, σ ARW Calculate variance data points for Allan;

[0028] Preferably, resetting the integrated navigation filter parameters obtained after initialization assignment using the Allan variance estimation result comprises the following steps:

[0029] The initialized combined navigation filter parameters are reset by the gyro angle wander coefficient or the accelerometer velocity wander coefficient; the specific expression is:

[0030]

[0031] Among them, Q ii It is the reset integrated navigation filter parameter.

[0032] Preferably, before resetting the initialized combined navigation filter parameters by using the gyro angle wander coefficient or the accelerometer speed wander coefficient, the method further includes the following steps:

[0033] The variance calculation points are logarithmically fitted to obtain the gyro angle wander coefficient or the accelerometer velocity wander coefficient; the specific expression is:

[0034]

[0035] Among them, log 10 It is the logarithm to base 10.

[0036] Preferably, after obtaining the filter parameter reconstruction result by resetting the combined navigation filter parameters, the method further includes the following steps:

[0037] Take off the drone and perform navigation operations on it. When the satellite information is valid, the filtering solution of the navigation system combining inertial navigation and satellite navigation is executed. Otherwise, it enters the pure inertial navigation solution.

[0038] Preferably, when the satellite information is valid, the filtering solution of the navigation system combining inertial navigation and satellite navigation is executed, otherwise the pure inertial navigation solution is entered, which includes the following steps:

[0039] The inertial solution attitude update is performed according to the angular velocity output by the inertial navigation system; the specific expression is:

[0040]

[0041] in, t m Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The navigation system at the moment is converted to quaternion, t m-1 Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The machine system converts quaternions at the time;

[0042] Inertial navigation is acquired through the strapdown inertial navigation velocity update equation; the specific expression is:

[0043]

[0044] in, t m Three-direction speed information under the real-time navigation system; t m-1 Three-direction velocity information under the time navigation system; Δt SINS The step size for strapdown inertial navigation solution; t m The relative force information output by the time inertial navigation sensor is added; t m-1 Gravity acceleration vector information in the time navigation system, g (m-1) t m-1 The gravitational acceleration in the altitude direction of the navigation system at all times; tm-1 The Earth's rotation information in the real-time navigation system, ω ie is the constant value of the Earth's rotation angular velocity 15°-h, L (m-1) t m-1 The geographical latitude of the moment; 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 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 the moment, H m-1 t m-1 The geographical altitude of the navigation system at the moment, V n(m-1) t m-1 The north navigation speed of the aircraft at this moment, V e(m-1) t m-1 The eastward navigation speed of the aircraft at this moment;

[0045] The real-time geographic latitude, longitude and altitude information is obtained through the position update equation of the strapdown inertial navigation system. The specific expression is:

[0046]

[0047] Among them, L (m) ,λ (m) and H (m) t m The geographical latitude, longitude and altitude under the current navigation system; L (m-1) ,λ (m-1) and H (m-1) t m-1 The geographical latitude, longitude and altitude under the time navigation system, sec is the secant function, V u(m-1) t m-1 The celestial navigation speed of the aircraft at any moment.

[0048] The present invention provides a filtering parameter reconstruction system for an airborne navigation system, comprising:

[0049] The data collection module is used to obtain the combined navigation filter parameters obtained after initialization and assignment in the extended Kalman filter (EKF) of the airborne navigation system; wherein the airborne navigation system is a navigation system combining inertial navigation and satellite navigation;

[0050] An alignment module is used to align the initial attitude angle of the airborne navigation system and obtain the angular velocity and acceleration of the inertial navigation system during alignment. The module determines the magnitude of the angular velocity of the inertial navigation system during alignment compared with a set angular velocity threshold, and removes invalid angular velocities and accelerations when the angular velocity of the inertial navigation system during alignment exceeds the set angular velocity threshold.

[0051] The reset module is used to perform Allan variance estimation using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration, reset the combined navigation filter parameters obtained after the initialization assignment through the Allan variance estimation result, and obtain the filter parameter reconstruction result through the reset combined navigation filter parameters.

[0052] The present invention provides a computer device, comprising a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the above-mentioned filtering parameter reconstruction method for an airborne navigation system.

[0053] The present invention provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for reconstructing filtering parameters of an airborne navigation system.

[0054] Compared with the prior art, the present invention has the following significant advantages:

[0055] The present invention is applied to the optimization of the combined navigation filtering algorithm of a navigation system combining airborne inertial navigation and satellite navigation. The combined navigation filtering parameters obtained after the airborne navigation system is initialized and assigned are obtained. The initial attitude angle of the airborne navigation system is aligned to obtain the angular velocity and acceleration during alignment. The angular velocity and acceleration are judged according to their magnitudes with a set angular velocity threshold. The invalid angular velocity and acceleration are removed. The remaining angular velocity and acceleration after the invalid angular velocity and acceleration are removed are used to perform Allan variance estimation to reset the combined navigation filtering parameters. The present invention realizes adaptive reconstruction design of the combined navigation filtering parameters by estimating the inertial navigation performance parameters before takeoff. There is no need to adaptively adjust the parameters during flight. The accuracy of the combined navigation filtering parameters and the filtering convergence of the combined navigation are improved by calibrating the initial attitude angle and removing invalid data, thereby greatly reducing the requirements for the computing power of the missile-borne computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 An overall flow chart of a method for reconstructing filtering parameters of an airborne navigation system according to the present invention is provided;

[0057] Figure 2 This is the test track diagram of the sports car;

[0058] Figure 3 This is a comparison chart of the filtering convergence between the method in the sports car test and the conventional filtering algorithm; Figure 3 (a) is a comparison diagram of the east misalignment angle in the filtered state and the system time waveform. Figure 3 (b) is a comparison diagram of the north misalignment angle in the filtered state and the system time waveform. Figure 3 (c) is a comparison diagram of the filtering state azimuth misalignment angle and the system time waveform. Figure 3 (d) is a comparison diagram of the eastward velocity in the filtered state and the system time waveform;

[0059] Figure 4 This is an additional figure comparing the filter convergence of the method and the conventional filtering algorithm in the sports car test; Figure 4 (a) is a comparison diagram of the north velocity in the filtered state and the system time waveform. Figure 4 (b) is a comparison diagram of the filtered state celestial velocity and the system time waveform;

[0060] Figure 5 This is a comparison chart of the solution accuracy between the method used in the sports car test and the conventional filtering algorithm; Figure 5 (a) is a comparison diagram of the pitch angle deviation and the time waveform. Figure 5 (b) is a comparison diagram of the heading angle deviation and the time waveform. Figure 5 (c) is a comparison diagram of the roll angle deviation and the time waveform. Figure 5 (d) is a comparison of the eastward velocity deviation and the time waveform;

[0061] Figure 6 This is an additional figure comparing the solution accuracy of the method used in the sports car test with that of the conventional filtering algorithm; Figure 6 (a) is a comparison diagram of the north velocity deviation and the time waveform. Figure 6 (b) is a comparison diagram of the celestial velocity deviation and the time waveform. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical solutions of the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0063] The purpose of the present invention is to provide a method, system, device, and storage medium for reconstructing filter parameters of an airborne navigation system. The method sets a takeoff process for a navigation system that combines airborne inertial navigation and satellite navigation, incorporates an inertial navigation parameter matrix estimation process into the pre-takeoff alignment phase, and uses Allan variance estimation to achieve adaptive reconstruction of the combined navigation filter parameters. This significantly improves the accuracy of calculating the combined navigation information in the combined inertial navigation and satellite navigation system, and significantly improves navigation accuracy under conditions of short-term satellite navigation loss of lock. Specifically:

[0064] like Figure 1 As shown, the present invention proposes a filter parameter reconstruction method for an airborne navigation system, which specifically includes the following steps:

[0065] Step S1: obtaining the combined navigation filter parameters obtained by initializing and assigning values ​​in the extended Kalman filter (EKF) of the airborne navigation system; wherein the airborne navigation system is a navigation system combining inertial navigation and satellite navigation.

[0066] S1 is specifically:

[0067] At the beginning of the filter parameter design, it was determined that the filtering method used by the navigation system combined with inertial navigation and satellite navigation is EKF filtering. The filtering equation is shown below. It calculates the actual deviation of the inertial navigation system at the moment of satellite navigation positioning to achieve the correction of inertial navigation information.

[0068] State one-step prediction equation:

[0069]

[0070] State estimation equation:

[0071]

[0072] Filter gain calculation equation:

[0073]

[0074] One-step forecast mean square error equation:

[0075]

[0076] Mean square error filter estimation equation:

[0077]

[0078] in, is the n-dimensional state vector of the navigation system at step k, Z k is the m-dimensional observation sequence of the navigation system, is the predicted value of the state vector at step k, n is the state vector of the navigation system at step k-1, φ k,k-1 is the n×n state transfer matrix of the navigation system, K k is the filter gain matrix, H k is the m×n dimensional observation matrix, P k / k-1 is the one-step prediction error variance matrix of the k-1 moment state, R k is the m×n dimensional symmetric positive definite variance matrix of the navigation system observation noise, P k-1 is the estimated error variance matrix of the k-1th step, Γ k-1 is the n×p dimensional noise input matrix, Q k-1 The p×p dimensional symmetric non-negative definite variance matrix of the navigation system process noise, P kis the estimated error variance matrix of the kth step, I is the identity matrix, and T is the transpose.

[0079] In some embodiments of the present invention, the assignment of filtering parameters in step S1 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.

[0080] Step S2: Align the initial attitude angle of the airborne navigation system, obtain the angular velocity and acceleration of the alignment inertial navigation, compare the angular velocity of the alignment inertial navigation with the set angular velocity threshold, and remove invalid angular velocities and accelerations when the angular velocity of the alignment inertial navigation is greater than the set angular velocity threshold.

[0081] S2 is specifically:

[0082] Align the initial attitude angle of the airborne navigation system, specifically:

[0083] The heading binding alignment method is used to align the initial attitude angle of the UAV with an airborne navigation system; the specific expression is:

[0084]

[0085] The initial attitude angle includes the initial alignment pitch angle θ and the initial alignment roll angle γ; Ax and Ay are the acceleration information in two directions; g is the local gravity acceleration information; Az is the acceleration information on the Z axis of the body; arcsin(·) is the inverse sine function, and arctan(·) is the inverse tangent function.

[0086] The relationship between the angular velocity of the drone's initial attitude angle and the set angular velocity threshold is determined. When the angular velocity is less than the set angular velocity threshold, the static sensor data of the inertial navigation system is stored, that is, the sensor data used for Allan estimation is stored; when the angular velocity is greater than the set angular velocity threshold, the static sensor data of the inertial navigation system is removed, that is, the sensor data at the moment when the angular velocity is greater than the set angular velocity threshold is removed.

[0087] Step S3: perform Allan variance estimation using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration, reset the integrated navigation filter parameters obtained after the initialization assignment using the Allan variance estimation result, and obtain the filter parameter reconstruction result using the reset integrated navigation filter parameters.

[0088] S3 specifically:

[0089] The remaining angular velocity and acceleration after removing invalid angular velocity and acceleration (i.e., the sensor acceleration and angular velocity during static alignment) are used to perform Allan variance estimation, and the initialized integrated navigation filter parameters are reset using the Allan variance estimation result, including the following steps:

[0090] Get the sequence of remaining angular velocity and acceleration after removing invalid angular velocity and acceleration; the specific expression is:

[0091]

[0092] Where τ0 is the data update period, N is the sample data point, They are the inertial sensor output data during the alignment of the airborne navigation system, and the inertial sensor output data are angular velocity and acceleration.

[0093] Get the variance of the sampling time during the alignment period as one data update period τ0 The specific expression is:

[0094]

[0095] Secondly, the sampling time is doubled continuously to obtain the doubled sampling time τ L , denoted by τ L =2*τ L-1 =2 L *τ0, When the final sequence length is not less than 2, the inertial navigation data sequence is obtained. is the inertial sensor output data during the alignment of the onboard navigation system at the kth sample data point, The inertial sensor output data during the alignment of the onboard navigation system at the k-1th sample data point. The specific expression is:

[0096]

[0097] in:

[0098]

[0099] Calculate the variance when the sampling time is τ1 as follows:

[0100]

[0101] Keep doubling the sampling time, denoted by τ L =2*τ L-1 =2 L *τ0, When the final sequence length is not less than 2, the inertial navigation data sequence is obtained; the specific expression is:

[0102]

[0103] Among them, N L is the sample data point after doubling the sampling time, τ L-1 is τ L Half the sampling time, N L-1 N L One-half the number of sample data points.

[0104] Get the sampling time as τ L Variance The specific expression is:

[0105]

[0106] By doubling the sampling time, a series of variance points corresponding to the sampling period are obtained. The gyro angle wander coefficient and the accelerometer velocity wander coefficient are obtained through a series of variance points corresponding to the sampling periods; the specific expressions are:

[0107]

[0108] Among them, N ARW is the gyro angle wander coefficient or the accelerometer velocity wander coefficient, σ ARW Calculate variance for Allan data points.

[0109] The initialized combined navigation filter parameters are reset by the gyro angle wander coefficient or the accelerometer velocity wander coefficient; the specific expression is:

[0110] Q ii =N ARW 2 .

[0111] Among them, Q ii It is the reset integrated navigation filter parameter.

[0112] Before resetting the initialized combined navigation filter parameters by using the gyro angle wander coefficient or the accelerometer velocity wander coefficient, the following steps are also included:

[0113] The variance calculation result points are logarithmically fitted to obtain the gyro angle wander coefficient or the accelerometer velocity wander coefficient. The specific expression is:

[0114]

[0115] Among them, log 10 It is the logarithm to base 10.

[0116] After obtaining the filter parameter reconstruction result by resetting the combined navigation filter parameters, the following steps are also included:

[0117] Take off the drone and perform navigation operations. When satellite information is valid, the combined inertial navigation and satellite navigation system performs filtering and solves, and uses the filtering results to correct the pure inertial solution results. Otherwise, the system enters the pure inertial navigation solution. That is, the combined navigation filtering calculation and inertial navigation solution are performed according to the satellite navigation positioning mark.

[0118] When the satellite information is valid, the filtering solution of the combined inertial navigation and satellite navigation system is executed. Otherwise, the pure inertial navigation solution is entered, including the following steps:

[0119] The inertial solution attitude update is performed according to the angular velocity output by the inertial navigation system; the specific expression is:

[0120]

[0121] Among them, Q = [q0,q1,q2,q3] is the spatial coordinate conversion quaternion, t m Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The navigation system at the moment is converted to quaternion, t m-1 Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The machine system converts quaternions at a certain time.

[0122] Inertial navigation is acquired through the strapdown inertial navigation velocity update equation; the specific expression is:

[0123]

[0124] in, t m Three-direction speed information under the real-time navigation system; t m-1 Three-direction velocity information under the time navigation system; Δt SINS The step size for strapdown inertial navigation solution; t m The relative force information output by the time inertial navigation sensor is added; t m-1 Gravity acceleration vector information in the time navigation system, g (m-1) t m-1 The gravitational acceleration in the altitude direction of the navigation system at all times; t m-1The Earth's rotation information in the real-time navigation system, ω ie is the constant value of the Earth's rotation angular velocity 15°-h, L (m-1) t m-1 The geographical latitude of the moment; 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 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 the moment, H m-1 t m-1 The geographical altitude of the navigation system at the moment, V n(m-1) t m-1 The north navigation speed of the aircraft at this moment, V e(m-1) t m-1 The eastward navigation speed of the aircraft at this moment.

[0125] The real-time geographic latitude, longitude and altitude information is obtained through the position update equation of the strapdown inertial navigation system. The specific expression is:

[0126]

[0127] Among them, L (m) ,λ (m) and H (m) t m The geographical latitude, longitude and altitude under the current navigation system; L (m-1) ,λ (m-1) and H (m-1) t m-1 The geographical latitude, longitude and altitude under the time navigation system, sec is the secant function, V u(m-1) t m-1 The celestial navigation speed of the aircraft at any moment.

[0128] The combined navigation filter parameter adaptive reconstruction method according to the present invention is used to conduct a sports car performance test on the ground. At the same time, the results of the AREKF filtering algorithm are compared with those of the conventional EKF filtering algorithm. The AREKF filtered sports car trajectory and the EKF filtered sports car trajectory are shown in the figure below. Figure 2 The test results are shown in Figure 3 、 Figure 4 、 Figure 5 and Figure 6 As shown, the following conclusions are drawn:

[0129] from Figure 2 It can be seen that the position accuracy of the adaptive reconstruction parameter filtering method in the maneuvering state is better than that of the conventional EKF filtering algorithm.

[0130] from Figure 3 (a) Figure 3 (b) Figure 3 (c) and Figure 3 (d), and Figure 4 (a) and Figure 4 From the comparison results of the filtering states shown in (b), it can be seen that the filtering convergence effect and stability of the adaptive reconstruction parameter filtering method are greatly improved compared with the conventional filtering method.

[0131] from Figure 5 (a) Figure 5 (b) Figure 5 (c) and Figure 5 (d), and Figure 6 (a) and Figure 6 From the comparison results of filtering accuracy shown in (b), it can be seen that the attitude and velocity solution accuracy of the adaptive reconstruction parameter filtering method is greatly improved, especially when the satellite signal is temporarily lost, the solution accuracy of the design method of the present invention has a more obvious advantage.

[0132] Based on the above method, the present invention provides a filter parameter reconstruction system for an airborne navigation system, comprising: a data collection module, an alignment module and a reset module.

[0133] Among them, the data collection module is used to obtain the combined navigation filter parameters obtained after initialization assignment in the extended Kalman filter EKF of the airborne navigation system; the airborne navigation system is a navigation system combined with inertial navigation and satellite navigation; the alignment module is used to align the initial attitude angle of the airborne navigation system, and obtain the angular velocity and acceleration of the inertial navigation at the time of alignment, and make a size judgment based on the angular velocity of the inertial navigation at the time of alignment and the set angular velocity threshold, and remove the invalid angular velocity and acceleration when the angular velocity of the inertial navigation at the time of alignment is greater than the set angular velocity threshold; the reset module is used to use the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration to perform Allan variance estimation, reset the combined navigation filter parameters obtained after initialization assignment through the Allan variance estimation result, and obtain the filter parameter reconstruction result through the reset combined navigation filter parameters.

[0134] The present invention also provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor executes the steps of a filter parameter reconstruction method for an airborne navigation system.

[0135] According to the disclosed embodiments, a computing device may communicate with one or more external devices (e.g., a keyboard, a pointing device, Bluetooth communications, etc.), or with any device that enables a computing device to communicate with one or more other computing devices (e.g., a router, a modem, etc.).

[0136] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for predicting the remaining time of an aircraft is implemented.

[0137] According to the disclosed embodiments, the storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0138] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0139] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A filter parameter reconstruction method for an airborne navigation system, characterized in that: The steps include: Obtain the combined navigation filter parameters obtained after initialization and assignment in the extended Kalman filter (EKF) of the airborne navigation system; wherein the airborne navigation system is a navigation system combining inertial navigation and satellite navigation; Align the initial attitude angle of the airborne navigation system and obtain the angular velocity and acceleration of the alignment inertial navigation. Determine the magnitude of the angular velocity of the alignment inertial navigation relative to the set angular velocity threshold, and remove invalid angular velocities and accelerations when the angular velocity of the alignment inertial navigation exceeds the set angular velocity threshold. An Allan variance estimation is performed using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration, and the integrated navigation filter parameters obtained after the initialization assignment are reset using the Allan variance estimation result, and the filter parameter reconstruction result is obtained using the reset integrated navigation filter parameters; The Allan variance estimation using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration includes the following steps: Get the sequence of remaining angular velocity and acceleration after removing invalid angular velocity and acceleration; the specific expression is: Where τ0 is the data update period, N is the sample data point, They are the inertial sensor output data during the alignment of the airborne navigation system, and the inertial sensor output data are angular velocity and acceleration; Get the variance of the sampling time during the alignment period as one data update period τ0 The specific expression is: Continuously double the sampling time to obtain the doubled sampling time τ L , denoted by τ L =2*τ L-1 =2 L *τ0, When the final sequence length is not less than 2, the inertial navigation data sequence is obtained. is the inertial sensor output data during the alignment of the onboard navigation system at the kth sample data point, is the inertial sensor output data during the alignment of the onboard navigation system at the k-1th sample data point; the specific expression is: Among them, N L is the sample data point after doubling the sampling time, τ L-1 is τ L Half the sampling time, N L-1 N L One-half the number of sample data points; Get τ L Variance The specific expression is: By doubling the sampling time, a series of variance points corresponding to the sampling period are obtained. The gyro angle wander coefficient and the accelerometer velocity wander coefficient are obtained through a series of variance points corresponding to the sampling periods; the specific expressions are: Among them, N ARW is the gyro angle wander coefficient or the accelerometer velocity wander coefficient, σ ARW Calculate variance data points for Allan; The method of resetting the integrated navigation filter parameters obtained after the initialization assignment using the Allan variance estimation result comprises the following steps: The initialized combined navigation filter parameters are reset by the gyro angle wander coefficient or the accelerometer velocity wander coefficient; the specific expression is: Among them, Q ii It is the reset integrated navigation filter parameter.

2. The method for reconstructing filter parameters of an airborne navigation system according to claim 1, wherein: The initial attitude angle of the airborne navigation system is aligned as follows: The heading binding alignment method is used to align the initial attitude angle of the UAV with an airborne navigation system; the specific expression is: The initial attitude angle includes the initial alignment pitch angle θ and the initial alignment roll angle γ; Ax and Ay are the acceleration information of the body in the X-axis and Y-axis directions; g is the local gravity acceleration information; Az is the acceleration information on the body on the Z-axis; arcsin(·) is the inverse sine function, and arctan(·) is the inverse tangent function.

3. The method for reconstructing filter parameters of an airborne navigation system according to claim 1, wherein: Before resetting the initialized combined navigation filter parameters by using the gyro angle wander coefficient or the accelerometer velocity wander coefficient, the following steps are also included: The variance calculation points are logarithmically fitted to obtain the gyro angle wander coefficient or the accelerometer velocity wander coefficient; the specific expression is: Among them, log 10 It is the logarithm to base 10.

4. The method for reconstructing filter parameters of an airborne navigation system according to claim 1, wherein: After obtaining the filter parameter reconstruction result by resetting the combined navigation filter parameters, the following steps are also included: Take off the drone and perform navigation operations on it. When the satellite information is valid, the filtering solution of the navigation system combining inertial navigation and satellite navigation is executed. Otherwise, it enters the pure inertial navigation solution.

5. The method for reconstructing filter parameters of an airborne navigation system according to claim 4, wherein: When the satellite information is valid, the filtering solution of the navigation system combining inertial navigation and satellite navigation is executed, otherwise the pure inertial navigation solution is entered, including the following steps: The inertial solution attitude update is performed according to the angular velocity output by the inertial navigation system; the specific expression is: in, t m Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The navigation system at the moment is converted to quaternion, t m-1 Quaternion conversion from time machine system to navigation system, t m-1 Time to t m The machine system converts quaternions at the time; Inertial navigation is acquired through the strapdown inertial navigation velocity update equation; the specific expression is: in, t m Three-direction speed information under the real-time navigation system; t m-1 Three-direction velocity information under the time navigation system; Δt SINS The step size for strapdown inertial navigation solution; t m The relative force information output by the time inertial navigation sensor is added; t m-1 Gravity acceleration vector information in the time navigation system, g (m-1) t m-1 The gravitational acceleration in the altitude direction of the navigation system at all times; t m-1 The Earth's rotation information in the real-time navigation system, ω ie is the constant value of the Earth's rotation angular velocity 15°-h, L (m-1) t m-1 The geographical latitude of the moment; 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 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 the moment, H m-1 t m-1 The geographical altitude of the navigation system at the moment, V n(m-1) t m-1 The north navigation speed of the aircraft at this moment, V e(m-1) t m-1 The eastward navigation speed of the aircraft at this moment; The real-time geographic latitude, longitude and altitude information is obtained through the position update equation of the strapdown inertial navigation system. The specific expression is: Among them, L (m) ,λ (m) and H (m) t m The geographical latitude, longitude and altitude under the current navigation system; L (m-1) ,λ (m-1) and H (m-1) t m-1 The geographical latitude, longitude and altitude under the time navigation system, sec is the secant function, V u(m-1) t m-1 The celestial navigation speed of the aircraft at any moment.

6. A system for the method for reconstructing filtering parameters of an airborne navigation system according to any one of claims 1 to 5, characterized in that: include: The data collection module is used to obtain the combined navigation filter parameters obtained after initialization assignment in the extended Kalman filter EKF of the airborne navigation system; The onboard navigation system is a navigation system that combines inertial navigation and satellite navigation; An alignment module is used to align the initial attitude angle of the airborne navigation system and obtain the angular velocity and acceleration of the inertial navigation system during alignment. The module determines the magnitude of the angular velocity of the inertial navigation system during alignment compared with a set angular velocity threshold, and removes invalid angular velocities and accelerations when the angular velocity of the inertial navigation system during alignment exceeds the set angular velocity threshold. The reset module is used to perform Allan variance estimation using the remaining angular velocity and acceleration after removing the invalid angular velocity and acceleration, reset the combined navigation filter parameters obtained after the initialization assignment through the Allan variance estimation result, and obtain the filter parameter reconstruction result through the reset combined navigation filter parameters.

7. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of the filtering parameter reconstruction method of an airborne navigation system as described in any one of claims 1 to 5.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a filter parameter reconstruction method for an airborne navigation system according to any one of claims 1 to 5 are implemented.

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