Guided shell model / inertial autonomous navigation method and system

Abstract

The application provides a guided shell model / inertial autonomous navigation method and system, which is suitable for the whole process autonomous navigation positioning of a guided shell. The framework mainly comprises two parts of filter model construction and filter method design. The system state equation is constructed according to a trajectory dynamics model. The measurement equation is constructed based on the coupling relationship between inertial devices and trajectory model parameters, and the output angular rate of a gyroscope and the output specific force of an accelerometer are taken as observation values. Based on the filter innovation information and historical data, a data-driven innovation UKF is designed, the measurement variance matrix can be adjusted in real time, the model / inertial parameter fusion precision is improved, and high-precision autonomous navigation of the guided shell is realized. The method is suitable for the navigation of the guided shell in a high dynamic environment.

Description

Technical Field

[0001] This invention belongs to the field of guided projectile navigation in high dynamic environments, and relates to a guided projectile model / inertial autonomous navigation method and system. Background Technology

[0002] Guided artillery shells are playing an increasingly important role in national security and weapon development. High-precision acquisition of navigation information is a key technology for guided artillery shells to achieve accurate target strikes, as navigation accuracy directly affects the accuracy of the shell's target hit. Because GNSS is a weak wireless signal, satellite signals are easily interfered with by complex environmental factors such as clouds, lightning, and solar storms in natural environments. In actual battlefield environments, satellite constellations are threatened by anti-satellite missiles, laser weapons, and electromagnetic shocks; satellite receivers are susceptible to electronic interference or deception, making accurate positioning impossible. Therefore, the development of guided artillery shells with inertial guidance as their core has become an urgent need in the new era of battlefield environments, particularly under satellite denial conditions. Research on inertial autonomous navigation methods for guided artillery shells in high-dynamic environments is of great significance.

[0003] Research on autonomous navigation of guided projectiles under satellite denial conditions primarily revolves around navigation methods that fuse information from various multi-source sensors. A research laboratory of a certain country's army proposed a multi-source fusion scheme combining accelerometers, gyroscopes, and magnetometers to achieve projectile attitude calculation and motion navigation. In 2015, a certain country launched a precision-robust inertial guided munitions project, developing advanced inertial microsensors to provide stable navigation performance under extreme conditions. Alicia Roux et al. of the Franco-German Institute in Saint-Louis, France, effectively estimated projectile trajectories using EKF and incomplete right-invariant EKF. This method focuses on the research of filtering estimation algorithms, with less analysis of the projectile itself and its environmental characteristics. Recently, Alicia Roux et al. proposed a deep Kalman filtering method, which estimates the trajectory of a projectile in a satellite-denied environment using only a reference magnetic field and an inertial measurement unit consisting of an accelerometer, gyroscope, and magnetometer. This method has achieved good results. However, it focuses on the application of deep learning in the field of Kalman filtering and lacks correlation with the actual projectile motion characteristics and environmental features. Therefore, it does not meet the requirements for inertial navigation accuracy in complex disturbance environments. Summary of the Invention

[0004] To address the problem of insufficient accuracy in inertial navigation under complex disturbance environments, this invention proposes a guided projectile model / inertial autonomous navigation method and system to improve navigation autonomy.

[0005] The technical solution to achieve the purpose of this invention is:

[0006] A guided projectile model / inertial autonomous navigation method includes:

[0007] Step 1: Construct the system state equations based on the six-degree-of-freedom rigid body ballistic model of the guided projectile;

[0008] Step 2: Analyze the coupling relationship between ballistic parameters and inertial device measurement information based on the system state equation, and construct the system measurement equation;

[0009] Step 3: Construct a guided projectile model / inertial autonomous navigation filter model based on the system state equation and system measurement equation;

[0010] Step 4: Based on filter innovation information and historical data, design a data-driven innovation UKF to estimate the guided projectile model / inertial autonomous navigation filter model, thereby realizing the autonomous navigation of the guided projectile.

[0011] Furthermore, the six-degree-of-freedom rigid body ballistic model of the guided projectile mentioned in step 1 is as follows:

[0012]

[0013] In the formula, These represent the projectile's center of mass velocity, velocity elevation angle, velocity direction angle, three-axis angular velocity about the center of mass, projectile axis elevation angle, projectile axis azimuth angle, projectile roll angle, and three-axis position of the projectile's center of mass, respectively. This represents the three-axis components of the external torque in the spring-axis coordinate system. This represents the three-axis components of the external force in the ballistic coordinate system. These represent the projectile's mass, polar moment of inertia, and equatorial moment of inertia, respectively.

[0014] The system state equation is:

[0015]

[0016] In the formula,

[0017] .

[0018] Furthermore, in step 2, the system measurement equations are constructed using the angular rate output by the gyroscope and the specific force output by the accelerometer as the observations.

[0019] Furthermore, the gyroscope output angular rate is:

[0020]

[0021] In the formula, The nonlinear equation representing the relationship between ballistic parameters and the gyroscope output angular rate. Let represent the i-th element in X. This refers to the angular rate output by the gyroscope.

[0022] The accelerometer outputs a specific force of:

[0023]

[0024] In the formula, The nonlinear equation representing the relationship between ballistic parameters and the specific force output by the accelerometer. , is triaxial acceleration, For east, north, and celestial velocities, Given the local gravitational acceleration, the state matrix is... .

[0025] Furthermore, the system measurement equation is as follows:

[0026]

[0027] in, This refers to the angular rate output by the gyroscope. For the accelerometer to output specific force, The nonlinear equation representing the relationship between ballistic parameters and the gyroscope output angular rate. Let represent the i-th element in X. The nonlinear equation representing the relationship between ballistic parameters and the specific force output by the accelerometer.

[0028] Furthermore, step 4 specifically includes:

[0029] First, initialize the state estimation. and state error covariance matrix The sigma point is calculated using the UT transformation.

[0030] The 4RK method is used to update the system state and state error covariance matrix over time, and then the UT transformation is performed again to generate a new sigma point set.

[0031] Measurement updates are performed based on the guided projectile model / inertial autonomous navigation filter model, and new information is calculated.

[0032] Based on the new information, assess the deviation between the system measurement prediction and the measurement new information, and determine the degree of deviation;

[0033] Based on the degree of deviation, the mean values ​​of the statistical angular rate and specific force deviation are used to design a data-driven adaptive measurement mean square error matrix.

[0034] Calculate the Kalman gain matrix and update the system state and covariance to achieve autonomous navigation of the guided projectile.

[0035] Furthermore, the 4RK method is used to update the system state over time as follows:

[0036]

[0037]

[0038] The weighting coefficients are calculated as follows:

[0039]

[0040] In the formula, i = 1, 2, ..., 2n, and the coefficients are... ,coefficient The range of values ​​is , .

[0041] Furthermore, the degree of deviation is:

[0042]

[0043] In the formula, This indicates the degree of deviation in the gyroscope's measurement of angular rate. This indicates the degree of deviation in the accelerometer's measurement of specific force. This represents the first three elements of the new information. This represents the last three elements of the new information.

[0044] Furthermore, the adaptive measurement mean square error matrix is:

[0045]

[0046] In the formula, , representing the elements on the diagonal of the measurement mean square error matrix. This represents the coefficient indicating the degree of deviation between strong and weak.

[0047] A guided projectile model / inertial autonomous navigation system, comprising:

[0048] The system state equation construction unit is used to construct the system state equation based on the six-degree-of-freedom rigid body ballistic model of the guided projectile.

[0049] The system measurement equation construction unit analyzes the coupling relationship between ballistic parameters and inertial device measurement information based on the system state equation and constructs the system measurement equation.

[0050] The filter model construction unit constructs a guided projectile model / inertial autonomous navigation filter model based on the system state equation and system measurement equation.

[0051] The navigation control unit, based on filter information and historical data, designs a data-driven information UKF to estimate the guided projectile model / inertial autonomous navigation filter model, thereby realizing the autonomous navigation of the guided projectile.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention comprises two parts: filter model construction and filter method design. Based on the ballistic dynamics model, a system state equation is constructed. Based on the coupling relationship between inertial devices and ballistic model parameters, measurement equations are constructed using the gyroscope output angular rate and the accelerometer output specific force as observations. Based on filter innovation information and historical data, a data-driven innovation UKF is designed, which can adjust the measurement variance matrix in real time, improving the model / inertial parameter fusion accuracy and achieving high-precision guided projectile autonomous navigation. The method of this invention is applicable to guided projectile navigation in high-dynamic environments. Attached Figure Description

[0054] Figure 1 This is a diagram of the guided projectile model / inertial autonomous navigation framework proposed in this invention.

[0055] Figure 2 A schematic diagram for constructing measurement equations for a system based on inertial measurement.

[0056] Figure 3 This is a schematic diagram of a data-driven adaptive measurement mean square error matrix. Detailed Implementation

[0057] The implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0058] Combination Figure 1 This embodiment provides a guided projectile model / inertial autonomous navigation method, including:

[0059] Combination Figure 2 This invention constructs system measurement equations based on inertial measurement. Based on the projectile angular rate, attitude information and projectile velocity output by the high-precision six-degree-of-freedom ballistic model, the angular rate and specific force information based on the inertial coordinate system are derived through coordinate transformation relationship, and the coupling relationship between inertial measurement and ballistic model parameters is constructed to realize the establishment of measurement equations.

[0060] The established six-degree-of-freedom rigid body ballistic model of the guided projectile is shown below.

[0061] (1)

[0062] In the formula, These represent the projectile's center of mass velocity, elevation angle, direction angle, three-axis angular velocity around the center of mass, elevation angle of the projectile axis, azimuth angle of the projectile axis, roll angle of the projectile body, and three-axis position of the projectile's center of mass, respectively. This represents the three-axis components of the external torque in the spring-axis coordinate system. This represents the three-axis components of the external force in the ballistic coordinate system. These represent the projectile's mass, polar moment of inertia, and equatorial moment of inertia, respectively.

[0063] The coordinate system transformation relationship coupled with equation (1) is shown below.

[0064] (2)

[0065] In the formula, These represent the directional angle of attack, the elevation angle of attack, and the rotation angle from the second missile axis coordinate system to the missile axis coordinate system, respectively.

[0066] make Equation (1) can be written as:

[0067] (3)

[0068] In the formula, .

[0069] According to equation (3), X is taken as the system state variable, and equation (3) is taken as the system state equation. The system state update process is completed by the fourth-order Runge-Kutta method.

[0070] Based on the ballistic model of a guided projectile, the relationship between its ballistic parameters and the measurement information from inertial devices is constructed, thereby obtaining the system measurement equations based on inertial measurement. The approach is as follows: Figure 1 As shown.

[0071] According to equation (1), the projectile's angular velocity can be measured using an inverted gyroscope.

[0072] (4)

[0073] In the formula, The nonlinear equation representing the relationship between ballistic parameters and the gyroscope output angular rate. Let X represent the i-th element in X. This refers to the angular rate output by the gyroscope.

[0074] Based on equation (1), taking the derivative of the last three equations, we can obtain...

[0075] (5)

[0076] According to the velocity differential equation of SINS:

[0077] (6)

[0078] In the formula, , is the triaxial acceleration; The speeds are represented by the speeds of the east, north, and sky directions, respectively. , The average radius of the Earth This is the angular rate of Earth's rotation; For location, represent latitude, longitude, and altitude, respectively. The triaxial specific force measured by the accelerometer This is the local gravitational acceleration. .

[0079] Combining equations (1), (5), and (6), the specific force output by the accelerometer can be obtained.

[0080] (7)

[0081] In the formula, The nonlinear equation representing the relationship between ballistic parameters and the specific force output by the accelerometer.

[0082] Combining equations (4) and (7), we can obtain the system measurement equation based on inertia.

[0083] (8)

[0084] In the formula, This refers to the angular rate output by the gyroscope. The accelerometer outputs the specific force.

[0085] See appendix Figure 3 This invention designs a data-driven adaptive adjustment method for the measurement variance matrix. Based on the definition of the degree of deviation according to the information, and based on the data distribution characteristics of the degree of deviation based on historical data, the degree of deviation is divided into strong deviation and weak deviation, so as to realize the real-time adjustment of the measurement variance matrix and improve the model / inertial filter fusion estimation accuracy.

[0086] Combining equations (3) and (8), the guided projectile model / inertial autonomous navigation filter model can be obtained as follows:

[0087] (9)

[0088] In the formula, .

[0089] For the filtering model (9), UKF is usually used for state estimation. The setting of the measurement covariance matrix is ​​directly related to the estimation accuracy. According to the observation equation, there is a strong nonlinear characteristic. Therefore, based on historical data, a data-driven innovation UKF (DIUKF) is proposed.

[0090] First, initialize the state estimation. and state error covariance matrix .

[0091] (10)

[0092] UT transformation, calculate sigma points.

[0093] (11)

[0094] In the formula, The range of values ​​is ; The state error covariance matrix The i-th column, i=1,2,…,n, is obtained using Cholesky decomposition.

[0095] According to equation (3), the 4RK method is used for time update.

[0096] (12)

[0097] (13)

[0098] In the formula, The system variance matrix and weight coefficients are calculated as follows:

[0099] (14)

[0100] In the formula, i = 1, 2, ..., 2n. , ,generally .

[0101] Based on equations (12) and (13), perform the UT transformation again to generate a new sigma point set.

[0102] (15)

[0103] The measurement is updated according to equations (4), (7), (8) and (15).

[0104] (16)

[0105] The information is calculated based on equation (16) and the output of the inertial device.

[0106] (17)

[0107] In the formula, , where represents the angular rate and relative force information output by the inertial device at time k.

[0108] Based on the new information, assess the deviation between the system's measurement prediction and the measurement new information, and define the degree of deviation:

[0109] (18)

[0110] In the formula, This indicates the degree of deviation in the gyroscope's measurement of angular rate. This indicates the degree of deviation in the force measured by the accelerometer. This represents the first three elements of the new information. This represents the last three elements of the new information.

[0111] The mean values ​​of angular velocity and specific force deviation are calculated according to equation (18).

[0112] (19)

[0113] The degree of deviation is recorded at each moment, and a data-driven adaptive measurement mean square error matrix is ​​designed. The idea is as follows: Figure 2 As shown.

[0114] Combination formula (19) and appendix Figure 2 Design a data-driven adaptive measurement mean square error matrix.

[0115] (20)

[0116] In the formula, , representing the elements on the diagonal of the measurement mean square error matrix. This represents the coefficient indicating the degree of strength deviation; the specific value is determined based on the actual situation.

[0117] The system covariance is calculated using equations (16) and (20).

[0118] (twenty one)

[0119] Calculate the Kalman gain matrix and update the system state and covariance.

[0120] (twenty two)

[0121] The DIUKF model is used to fuse the model with inertia, enabling autonomous navigation of guided projectiles.

[0122] This invention also provides a guided projectile model / inertial autonomous navigation system, comprising:

[0123] The system state equation construction unit is used to construct the system state equation based on the six-degree-of-freedom rigid body ballistic model of the guided projectile.

[0124] The system measurement equation construction unit analyzes the coupling relationship between ballistic parameters and inertial device measurement information based on the system state equation and constructs the system measurement equation.

[0125] The filter model construction unit constructs a guided projectile model / inertial autonomous navigation filter model based on the system state equation and system measurement equation.

[0126] The navigation control unit, based on filter information and historical data, designs a data-driven information UKF to estimate the guided projectile model / inertial autonomous navigation filter model, thereby realizing the autonomous navigation of the guided projectile.

[0127] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A guided projectile model / inertial autonomous navigation method, characterized in that, include: Step 1: Construct the system state equations based on the six-degree-of-freedom rigid body ballistic model of the guided projectile; Step 2: Analyze the coupling relationship between ballistic parameters and inertial device measurement information based on the system state equation, and construct the system measurement equation; Step 3: Construct a guided projectile model / inertial autonomous navigation filter model based on the system state equation and system measurement equation; Step 4: Based on the filter innovation information and historical data, design a data-driven innovation UKF to estimate the guided projectile model / inertial autonomous navigation filter model, and realize the autonomous navigation of the guided projectile. The six-degree-of-freedom rigid body ballistic model of the guided projectile mentioned in step 1 is as follows: In the formula, These represent the projectile's center of mass velocity, velocity elevation angle, velocity direction angle, three-axis angular velocity about the center of mass, projectile axis elevation angle, projectile axis azimuth angle, projectile roll angle, and three-axis position of the projectile's center of mass, respectively. This represents the three-axis components of the external torque in the spring-axis coordinate system. This represents the three-axis components of the external force in the ballistic coordinate system. These represent the projectile's mass, polar moment of inertia, and equatorial moment of inertia, respectively. The system state equation is: In the formula, ； Step 2 uses the angular rate output by the gyroscope and the specific force output by the accelerometer as the observables to construct the system measurement equations. The gyroscope outputs an angular rate of: In the formula, The nonlinear equation representing the relationship between ballistic parameters and the gyroscope output angular rate. Let represent the i-th element in X. This refers to the angular rate output by the gyroscope. The accelerometer outputs a specific force of: In the formula, The nonlinear equation representing the relationship between ballistic parameters and the specific force output by the accelerometer. , is triaxial acceleration, For east, north, and celestial velocities, Given the local gravitational acceleration, the state matrix is... ; The system measurement equation is: in, This refers to the angular rate output by the gyroscope. For the accelerometer to output specific force, The nonlinear equation representing the relationship between ballistic parameters and the gyroscope output angular rate. Let represent the i-th element in X. The nonlinear equation representing the relationship between ballistic parameters and the specific force output by the accelerometer; Step 4 specifically includes: First, initialize the state estimation. and state error covariance matrix The sigma point is calculated using the UT transformation. The 4RK method is used to update the system state and state error covariance matrix over time, and then the UT transformation is performed again to generate a new sigma point set. Measurement updates are performed based on the guided projectile model / inertial autonomous navigation filter model, and new information is calculated. Based on the new information, assess the deviation between the system measurement prediction and the measurement new information, and determine the degree of deviation; Based on the degree of deviation, the mean values ​​of the statistical angular rate and specific force deviation are used to design a data-driven adaptive measurement mean square error matrix. Calculate the Kalman gain matrix and update the system state and covariance to achieve autonomous navigation of the guided projectile. The new information is as follows: In the formula, , where represents the angular rate and specific force information output by the inertial device at time k. .

2. The guided projectile model / inertial autonomous navigation method according to claim 1, characterized in that, The 4RK method is used to update the system state and state error covariance matrix over time. In the formula, The system variance matrix, variables The weighting coefficients are calculated as follows: In the formula, the coefficients The range of values ​​is , is a coefficient.

3. The guided projectile model / inertial autonomous navigation method according to claim 1, characterized in that, The degree of deviation is: In the formula, This indicates the degree of deviation in the gyroscope's measurement of angular rate. This indicates the degree of deviation in the accelerometer's measurement of specific force. This represents the first three elements of the new information. This represents the last three elements of the new information.

4. The guided projectile model / inertial autonomous navigation method according to claim 1, characterized in that, The adaptive measurement mean square error matrix is: In the formula, , representing the elements on the diagonal of the measurement mean square error matrix. This represents the coefficient indicating the degree of deviation between strong and weak.

5. A guided projectile model / inertial autonomous navigation system implementing the method of any one of claims 1-4, characterized in that, include: The system state equation construction unit is used to construct the system state equation based on the six-degree-of-freedom rigid body ballistic model of the guided projectile. The system measurement equation construction unit analyzes the coupling relationship between ballistic parameters and inertial device measurement information based on the system state equation and constructs the system measurement equation. The filter model construction unit constructs a guided projectile model / inertial autonomous navigation filter model based on the system state equation and system measurement equation. The navigation control unit, based on filter information and historical data, designs a data-driven information UKF to estimate the guided projectile model / inertial autonomous navigation filter model, thereby realizing the autonomous navigation of the guided projectile.

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