Large-scale differential rotation guided projectile navigation method and device under constraint of multiple prefabricated ballists

Abstract

The invention discloses a large-scale differential rotation guided projectile navigation method and device under the constraint of multiple prefabricated ballists. The method comprises the following steps: under the condition that a GNSS (Global Navigation Satellite System) signal is available, navigating a guided projectile by utilizing the GNSS signal and INS (Inertial Navigation System) data; and under the condition that the GNSS signal is unavailable, screening out an optimal prefabricated trajectory by using a correlation analysis result of the prefabricated trajectory and the estimated trajectory obtained when the GNSS is available, and navigating the guided projectile based on the optimal prefabricated trajectory and the INS data. The technical problem of inaccurate navigation of the guided projectile is solved.

Description

Technical Field

[0001] The present invention relates to the field of navigation, and in particular to a navigation method and device for a large-scale differential spin guided projectile under the constraints of multiple prefabricated trajectories. Background Art

[0002] The precise navigation and positioning of guided ammunition is the core and key to achieving precision guidance. The currently widely used navigation system is generally based on the combined navigation system of the inertial navigation system (INS) and the global navigation satellite system (GNSS). However, in actual wars, satellite signals are easily interfered with by the opposing party, which significantly reduces the accuracy of navigation and positioning. Satellite ground-based augmentation technology can provide projectiles with short-term effective satellite signals after leaving the muzzle. Therefore, how to provide accurate and reliable navigation information for guided ammunition after the GNSS is challenged is a crucial issue.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiment of the present invention provides a method and device for navigating a large-scale differential-spin guided projectile under the constraints of multiple prefabricated trajectories, so as to at least solve the technical problem of inaccurate navigation of the guided projectile.

[0005] According to one aspect of an embodiment of the present invention, a method for navigating a large-scale differential-spin guided projectile under multiple prefabricated trajectory constraints is provided, comprising: when a GNSS signal is available, navigating the guided projectile using the GNSS signal and INS data; when a GNSS signal is not available, screening out the best prefabricated trajectory using the correlation analysis results between the prefabricated trajectory and the estimated trajectory obtained when the GNSS is available, and navigating the guided projectile based on the best prefabricated trajectory and INS data.

[0006] According to another aspect of an embodiment of the present invention, there is also provided a large-scale differential-spin guided artillery shell navigation device under multiple prefabricated trajectory constraints, comprising: a GNSS-INS navigation module, configured to use GNSS signals and INS data to navigate the guided artillery shell when the GNSS signal is available; an optimal prefabricated trajectory-INS navigation module, configured to use the correlation analysis results of the prefabricated trajectory and the estimated trajectory obtained when the GNSS signal is available to screen out the optimal prefabricated trajectory when the GNSS signal is not available, and navigate the guided artillery shell based on the optimal prefabricated trajectory and INS data.

[0007] In an embodiment of the present invention, when GNSS signals are available, the GNSS signals and INS data are used to navigate the guided artillery shells; when GNSS signals are unavailable, the correlation analysis results between the prefabricated trajectory and the estimated trajectory obtained when GNSS is available are used to screen out the best prefabricated trajectory, and the guided artillery shells are navigated based on the best prefabricated trajectory and INS data, thereby solving the technical problem of inaccurate navigation of guided artillery shells. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0009] Figure 1 is a flow chart of navigation of a guided projectile with prefabricated trajectory constraints according to an embodiment of the present invention;

[0010] Figure 2 This is an appearance structure diagram of a large-scale differential-spin guided projectile according to an embodiment of the present invention;

[0011] Figure 3 is a schematic diagram of a flight process of a guided munition according to an embodiment of the present invention;

[0012] Figure 4 is a structural diagram of a guided artillery shell navigation strategy with multiple prefabricated trajectory constraints according to an embodiment of the present invention;

[0013] Figure 5 is a prefabricated trajectory diagram according to an embodiment of the present invention;

[0014] Figure 6 is a diagram of an ideal angular velocity of a virtual gyroscope according to an embodiment of the present invention, wherein (a) is an ideal angular velocity of the virtual gyroscope along the x and z axes, and (b) is an ideal angular velocity of the virtual gyroscope along the y axis;

[0015] Figure 7 is a prefabricated trajectory diagram under different wind speed conditions according to an embodiment of the present invention;

[0016] Figure 8 is an east-north plane projection diagram of a prefabricated trajectory under different wind speed conditions according to an embodiment of the present invention;

[0017] Fig. 9 is a correction effect diagram of a correction function according to an embodiment of the present invention;

[0018] Fig.10 is a structural schematic diagram of a large-scale differential spin guided projectile navigation device under multi-prefabricated trajectory constraints according to an embodiment of the present invention;

[0019] Fig.11A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of a large-scale differential-spin guided artillery projectile navigation method under multiple prefabricated trajectory constraints is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] Conventional ammunition guidance is the development trend of modern warfare, and the precise navigation and positioning of guided ammunition is the core and key to improving its strike accuracy. Guided artillery shells under conventional systems face the problem that GNSS signals are challenged and random wind disturbances and other complex environmental influences make it difficult to effectively obtain accurate navigation parameters during flight. This application uses a large-scale differential spin guided artillery shell with tail despin as a carrier to provide a large-scale differential spin guided artillery shell navigation method under the constraint of multiple prefabricated trajectory information. The target uses prefabricated trajectory information to assist the artillery shell inertial navigation system (INS) to achieve more accurate navigation and positioning when the GNSS signal is challenged. First, an improved nearest neighbor method is proposed. By weighting the measurement indicators of artillery shell speed, position, and gyro signal, the statistical distance association criterion is optimized. When the GNSS signal is temporarily available, the prefabricated trajectory that can replace the GNSS when the GNSS fails is selected from multiple prefabricated trajectories under different wind speed conditions. Then, combined with the dynamic characteristics of the artillery shell, a trajectory yaw information correction function is designed to correct the selected prefabricated trajectory yaw information to make it more consistent with the current artillery shell flight state. Finally, the Kalman filter was adaptively and robustly modified, the Kalman gain was adjusted according to the actual process, and the data fusion of the prefabricated trajectory and the inertial navigation calculation results was optimized to achieve accurate navigation and positioning of the guided projectile when the GNSS was challenged. The flight experiment results show that the root mean square error of the spatial position of the method proposed in this application is 3.8m, and the method has certain feasibility and accuracy.

[0024] In order to achieve more accurate navigation and positioning of guided artillery shells in a GNSS challenge environment. This application proposes a multi-prefabricated trajectory information constrained differential spin guided artillery shell navigation method. In order to avoid misjudgment of screening the best prefabricated trajectory, this application provides an improved nearest neighbor method, which distributes the measurement indicators of shell speed and position through weights, and introduces the gyro signal to optimize the statistical distance association criterion. When the GNSS signal is temporarily available, the prefabricated trajectory that can replace the GNSS after GNSS failure is screened out from the prefabricated trajectories under multiple different crosswind wind speed conditions. Then, combined with the flight dynamics characteristics of the projectile, a trajectory yaw information correction function is designed to correct the screened prefabricated trajectory yaw information to make it more in line with the current shell flight state. Finally, this application makes an adaptive robust modification to the Kalman filter, so that the filter gain is adjusted according to the actual situation, and the data fusion of the prefabricated trajectory and the inertial navigation calculation results is optimized, so as to achieve more accurate navigation and positioning of the guided artillery shell when the GNSS is challenged.

[0025] The multi-prefabricated trajectory information-constrained differential-spin guided projectile navigation method provided in this application has two working modes. When the GNSS signal is available, the system works in the GNSS / INS combined navigation mode. When the GNSS signal is not available, the prefabricated trajectory is used for navigation. Specifically, Figure 1 As shown, the navigation method includes the following steps:

[0026] Step S102: When the GNSS signal is available, the GNSS signal and INS data are used to navigate the guided projectile.

[0027] The navigation method provided in this application is applied to a new type of large-scale differential spin tail controlled guided projectile, which is similar to the traditional grenade structure and consists of a fuze, a projectile body, a warhead and a stern. Figure 2 As shown. The electronic devices and pulse engines used for trajectory correction of the projectile are all located on the stern. The integrated design method is adopted, which is easy to disassemble and assemble, reducing the cost and difficulty of modifying the old projectile. The projectile body, warhead and fuze are collectively called the forebody, which is connected to the stern by a bearing, and the two can rotate relative to each other. The stern is equipped with a satellite antenna and inertial measurement assembly for projectile positioning, an oblique anti-rotation wing for stern anti-rotation, and a pulse engine for trajectory correction. The forebody maintains high-speed rotation during flight to provide gyroscopic stability, so that the projectile remains stable during flight. The stern can quickly reduce rotation under the action of the anti-rotation wing, forming a large-scale differential rotation state, providing a more stable measurement environment for the measurement device located at the stern, and improving the positioning accuracy of navigation.

[0028] First, the first position and velocity information of the guided projectile are determined based on the GNSS signal, and the second position and velocity information of the guided projectile are solved based on the INS data; then, a GNSS-INS measurement residual is generated based on the first position and velocity information and the second position and velocity information; finally, a KF filter is driven based on the GNSS-INS measurement residual to estimate the position, velocity and attitude information of the guided projectile.

[0029] Step S104, when the GNSS signal is unavailable, the best prefabricated trajectory is screened out using the correlation analysis result between the prefabricated trajectory and the estimated trajectory obtained when the GNSS is available, and navigation is performed for the guided projectile based on the best prefabricated trajectory and INS data.

[0030] Firstly, the best prefabricated trajectory is selected based on the correlation analysis results between the prefabricated trajectory and the estimated trajectory obtained when GNSS is available;

[0031] Then, a yaw information correction function is constructed based on the flight dynamic characteristics of the guided projectile, and the yaw information correction function is used to correct the optimal prefabricated trajectory information; for example, the yaw information correction function is constructed based on the lateral deviation position before and after correction in the prefabricated trajectory geographic coordinate system, the prefabricated trajectory yaw angle, and the estimated trajectory yaw angle.

[0032] Next, the residual is obtained by using the optimal prefabricated trajectory information corrected by the yaw information correction function and the position and velocity information solved by INS;

[0033] Finally, the position, velocity and attitude information of the guided projectile are estimated by using the residual to drive the robust KF. The robust KF is obtained based on the following: calculating the statistical distance of the optimal prefabricated trajectory, and using the solved statistical distance as the adaptive outer loop parameter in the filter structure that is connected to the external measurement; using the adaptive outer loop parameter to construct the k-time measurement noise covariance gain coefficient, and using the measurement noise covariance gain coefficient as the robust inner loop adjustment parameter of the filter to obtain the robust KF.

[0034] Among them, the measurement noise covariance gain coefficient has the ability to adaptively adjust according to the statistical distance. When the statistical distance is greater than the product of the root mean square of the statistical distance and a preset constant coefficient, the measurement noise covariance gain coefficient drives the root mean square to increase rapidly, so that the filtering gain becomes rapidly smaller; when the statistical distance is less than or equal to the product of the root mean square of the statistical distance and a preset constant coefficient, the measurement noise covariance gain coefficient remains unchanged at 1.

[0035] The KF-based GNSS / INS combined navigation algorithm has been widely used in the past few decades. However, for the navigation method of guided artillery shells, due to its application in a war environment, the GNSS signal is easily affected by the projectile's own movement and the purposeful interference of the opposing party, which significantly reduces the navigation positioning accuracy. Through satellite ground-based augmentation technology, it can be guaranteed that there is a relatively reliable satellite signal for a short period of time after the projectile leaves the muzzle. The prefabricated trajectory of the projectile is a trajectory set before the projectile is launched based on the environmental characteristics and the ballistic model. It contains the projectile's position, speed, and attitude information. If the projectile is not strongly disturbed during flight, the actual trajectory will fit the prefabricated trajectory. The flight process of a guided artillery shell is like Figure 3 shown.

[0036] When the GNSS signal is not available, the present application utilizes the navigation system model to screen out the best prefabricated trajectory based on the correlation analysis results between the prefabricated trajectory and the estimated trajectory obtained when GNSS is available, and corrects the yaw information of the best prefabricated trajectory by using the yaw information correction function constructed in combination with the flight dynamic characteristics of the projectile. The residual is obtained by using the corrected prefabricated trajectory position and velocity information and the position and velocity information solved by INS to drive the robust KF to obtain high-precision projectile navigation and positioning information.

[0037] Regardless of whether GNSS is effective or not, INS (Inertial Navigation System) navigation is required. The following describes the INS navigation system model in detail.

[0038] The navigation error model in the navigation system has a great impact on the accuracy and performance of the entire system. The error model of the navigation system of a large-scale differential spin guided projectile under the constraints of multiple prefabricated trajectories is a model complex composed of multiple sensors, including INS, GNSS, prefabricated trajectories, etc. INS can provide 15-dimensional (15-D) navigation information to the navigation system at high frequency and continuously, so its position, velocity and attitude error model is usually used as the main part of the system state equation.

[0039] 1) Navigation system state model

[0040] The navigation coordinate system of this application is defined in the East-North-Sky (ENU) geographic coordinate system, the guided projectile carrier coordinate system is right-front-up

[10]

[24] , and the system state equation is

[0041]

[0042] Here X(t) is the error state vector of the 15-D large-scale differential spin guided projectile X(t) = [δr 3×1 δv 3×1 ε 3×1 bg 3× 1ba 3×1 ] T ,δr 3×1 =[△r E △r N △r U ] T is the position error vector, δv 3×1 =[△v E △v N △v U ] T is the velocity error vector, ε 3×1 =[△ε E △ε N △ε U ] T is the attitude angle error, bg 3×1 =[Bg E Bg N Bg U ] T is the missile-borne gyroscope zero bias vector, ba 3×1 =[Ba E Ba N Ba U ] T is the zero bias vector of the missile-borne accelerometer. W(t) is the system process noise vector W(t) = [ω v ω ε ω bg ω ba ] T ,ω vand ω ε are the measurement white noise of the missile-borne accelerometer and gyroscope, ω bg and ω ba are the zero bias of the missile-borne gyroscope and accelerometer, respectively, which are generally assumed to be the driving white noise of the first-order Gaussian Markov process. F(t) is the system dynamic matrix, which includes the position, velocity, attitude of the large-scale differential-guided artillery projectile INS, as well as the error model of the three-axis gyroscope and three-axis accelerometer, which can be expressed as

[0043]

[0044] G(t) is the system noise propagation matrix, which can be expressed as

[0045]

[0046] in, Represents the rotation matrix from the carrier coordinate system to the navigation coordinate system, I 3×3 is the identity matrix.

[0047] Then, the present application can express the discrete form of the state equation as

[0048]

[0049] Right now

[0050]

[0051] Here, T is the sampling time.

[0052] 2) Navigation system measurement model

[0053] Depend on Figure 4 It can be seen that the navigation and positioning strategy proposed in this application is roughly divided into two parts: when GNSS is valid, building a GNSS / INS system; when GNSS fails, building an optimal prefabricated trajectory / INS system.

[0054] The prepared trajectory data will be described in detail below.

[0055] In order to solve the problem of how to provide an accurate navigation and positioning method for large-scale differential-spin guided projectiles in an environment with challenging GNSS signals, this application proposes a guided projectile navigation method based on multi-prefabricated trajectory information constraints. In this method, the prefabricated trajectory information of the guided projectile under different wind speed conditions and the inferred virtual sensor information are utilized.

[0056] The prefabricated trajectory used in the method of this application is based on a seven-degree-of-freedom (7-DOF) projectile dynamics model. This model is used to calculate the high-fidelity trajectory of a new type of double-spin tail controlled ammunition. The new type of double-spin tail controlled ammunition is the application object of this application. The trajectory model can output the prefabricated trajectory information used in this application by setting the initial information before the projectile is launched, such as the initial position, initial velocity, firing angle, wind speed, etc., and calculating and outputting the prefabricated trajectory information used in this application, including: position, velocity, attitude, and corresponding time information. Figure 5 shown.

[0057] In addition to using the position, velocity, and attitude information of the prefabricated trajectory, this application also uses the attitude information to infer the virtual gyroscope output signal. According to the Euler equation, the ideal output angular rate of the gyroscope is as follows:

[0058]

[0059] Here, are the ideal angular rates of the right, front and upper axes of the gyroscope at time k, respectively, k and θ k are the roll angle and yaw angle of the projectile at time k, is the angular rate of change of the pitch and yaw angle of the projectile at time k, t s is the sampling interval. is the rolling angular velocity, which is obtained from the rolling velocity of the projectile output by the ballistic model. The ideal angular velocity output result of the virtual gyroscope is as follows: Figure 6 shown.

[0060] The measurement equation will be described below.

[0061] The general discrete form of the measurement equation is

[0062] Z k =H k X k +V k (7)

[0063] Here, H k is the measurement matrix, V k The measurement noise is usually a Gaussian white noise vector sequence with zero mean.

[0064] For the GNSS / INS system, the measurement vector consists of the difference between the position and velocity information calculated by the INS and the position and velocity information measured by the GNSS, which can be expressed as

[0065]

[0066] Here and are the position and velocity vectors solved by INS, and are the position and velocity vectors measured by GNSS respectively.

[0067] The GNSS / INS system measurement equation is:

[0068]

[0069] For the prefabricated trajectory / INS system, the measurement equation is composed of the difference between the position and velocity information solved by the INS and the projectile position and velocity information described by the prefabricated trajectory at the current moment, which can be expressed as

[0070]

[0071] Here and They are respectively the projectile position and velocity vector described by the prefabricated trajectory at the current moment.

[0072] The measurement equation of the prefabricated trajectory / INS system is:

[0073]

[0074] The Kalman filter and the robust Kalman filter will be described below.

[0075] from Figure 4 It can be seen that the Kalman filter and its adaptive robust Kalman filter with adaptive robust correction are used in the navigation and positioning strategy of guided artillery projectiles constrained by multiple prefabricated trajectory information proposed in this application. The Kalman filter is mainly responsible for fusing INS with GNSS when GNSS is valid to obtain accurate projectile navigation and positioning information. At the same time, according to the results of the correlation analysis of multiple prefabricated trajectories, the best prefabricated trajectory available when GNSS fails is screened out in advance. When the GNSS signal is unavailable, the screened prefabricated trajectory is fused with INS through adaptive robust Kalman filtering to obtain relatively accurate navigation and positioning information.

[0076] 1) Correlation analysis of multiple hypothetical prefabricated trajectories

[0077] The purpose of prefabricated trajectory correlation analysis is to solve the problem of selecting a relatively accurate navigation measurement source for guided artillery shells during the period when GNSS signals fail.

[0078] Before the guided projectile is launched, setting an accurate prefabricated trajectory will fit the actual trajectory of the projectile. However, when the projectile encounters crosswind interference after launch, the actual trajectory will deviate from the prefabricated trajectory. In order to obtain a prefabricated trajectory close to the actual trajectory, by assuming multiple prefabricated trajectories with different crosswind speeds, the nearest neighbor data association method is used and improved, and the best prefabricated trajectory is screened out within the time period when the GNSS signal is available. The main steps are as follows.

[0079] Assuming that the GNSS signal is updated at the kth time, the difference between the measurement information of the i-th prefabricated trajectory and the measurement estimate of the GNSS-INS system is expressed as

[0080]

[0081] In the formula It is the measurement value formed by the difference between the position and velocity information solved by INS at time k and the position and velocity of the i-th prefabricated trajectory at time k; A priori state estimation of the GNSS-INS system at time k.

[0082] Assume S k is the new information covariance of the GNSS-INS system at time k, which can be expressed as

[0083]

[0084] Where P k,k-1 is the a priori error covariance matrix of the GNSS-INS system at time k; R k is the measurement noise covariance matrix at time k.

[0085] Then the general statistical distance between the i-th prefabricated trajectory and the estimated trajectory at time k is

[0086]

[0087] The measurement information used in the statistical distance only includes speed and position, and the weight of each indicator is the same. The measurement information is not fully utilized, which leads to the situation that multiple prefabricated trajectories have the same statistical distance as the estimated trajectory at certain moments, affecting the selection of the final optimal prefabricated trajectory.

[0088] Therefore, it is proposed to introduce gyroscope data into the statistical distance calculation, which is equivalent to constraining the angle. Considering that multiple prefabricated trajectories are generated under different crosswind speed conditions, the influence of crosswind on the trajectory mainly acts on the yaw information, and the yaw information should be the main focus. Therefore, the statistical distance formula is reconstructed as

[0089]

[0090] in and is the virtual gyroscope x, y, z three-axis angular rate signal of the i-th prefabricated trajectory at time k, and is the angular rate signal of the missile-borne gyroscope x, y, and z axes at time k, Π is the introduced weight diagonal matrix, which is set according to actual needs and is in the form of

[0091]

[0092] Here, and are the position weights for the east, north, and sky directions, respectively. and are the speed weights for the east, north, and sky directions, respectively, and they satisfy

[0093]

[0094] In order to ensure that the measurement effect provided by the screened prefabricated trajectory to the filtering system is close to that of GNSS, the present application incorporates the statistical distance between GNSS and the estimated trajectory into the design of the screening threshold Υ.

[0095]

[0096] Here Mdf gnss,k is the time point when the GNSS signal starts to be effective, and is the mean statistical distance between the GNSS and the estimated trajectory when it is continuously effective until the current time k, and τ is the length of this period of time.

[0097] The threshold at time k can be expressed as

[0098] γ k =Mdf gnss,k +df gnss,max ·rand (19) here df gnss,max In the above τ time period, df gnss The maximum value of .

[0099] Therefore, the present application can define the threshold rule at time k as

[0100] df i,k <γ k (20)

[0101] If the i-th prefabricated trajectory at time k meets this rule, it will be retained until time k+1. If it does not meet this rule, it will be deleted from the system and will no longer be used for subsequent screening.

[0102] When the GNSS signal fails, the RMS value is calculated based on the historical statistical distance of the retained prefabricated trajectory. The prefabricated trajectory corresponding to the smallest one is selected as the best prefabricated trajectory.

[0103]

[0104] 2) Prefabricated trajectory correction model

[0105] In order to reduce the amount of calculation when selecting the best prefabricated trajectory, the wind speed interval when generating the prefabricated trajectory cannot be very small, and the interval between the minimum wind speed and the maximum wind speed should not be very large. This also causes a certain deviation between the wind speed corresponding to the final prefabricated trajectory and the wind speed of the actual trajectory. Set the firing direction to due north, the firing angle to 30°, and the initial velocity to 611m / s. The trajectory trajectories under different crosswind speeds are as follows: Figure 7 and Figure 8 shown.

[0106] It can be seen that the influence of crosswind on the trajectory is mainly concentrated in the lateral deviation, so it is necessary to correct the yaw information of the selected prefabricated trajectory.

[0107] According to the conversion relationship between the velocity coordinate system and the navigation coordinate system, the kinematic equations of the projectile mass center motion are obtained as follows:

[0108]

[0109] in and are the rates of change of the right, front and top positions of the ground coordinate system, ψ a is the velocity elevation angle, ψ d is the velocity azimuth, and v is the velocity of the projectile's center of mass.

[0110] It can be seen that the speed in the yaw direction is related to the velocity direction angle and the current velocity. According to the flight characteristics of the projectile, during the stable flight of the projectile, its velocity direction is similar to the slow motion items such as the pitch angle and the yaw angle. If the trajectory is divided into multiple straight segments, the pitch angle and the yaw angle in each segment swing with the velocity angle curve as the baseline

[27] . Therefore, the velocity azimuth angle ψ d is approximately the yaw angle ψ, then Approximately

[0111] In order to weaken the deviation between the prefabricated trajectory and the actual trajectory yaw information caused by the crosswind speed error, the above dynamic equation and the angle approximation relationship are used. Considering the high flight speed of the projectile, the small wind speed error has little effect on the size of the projectile center of mass velocity v. It is assumed that the current combined speed of the corrected prefabricated trajectory is similar to the current combined speed of the corrected target prefabricated trajectory. Therefore, this application uses the filtering to estimate the yaw angle of the trajectory to construct the prefabricated trajectory yaw information correction function:

[0112]

[0113] In the formula is the lateral deviation position before and after correction in the prefabricated trajectory geographic coordinate system, ψ pt is the prefabricated trajectory yaw angle, ψ rTo estimate the trajectory yaw angle, the yaw information corrected by this function is compared with the uncorrected yaw information. Fig. 9 As shown in the figure, the crosswind speed of the corrected prefabricated trajectory is 2m / s, and the correction target is to filter the estimated trajectory. Assuming that the prefabricated trajectory has a wind speed of 3m / s, it can be seen that the function construction is effective.

[0114] 3) Adaptive Robust Kalman Filter

[0115] Since the motion environment of the projectile is a highly dynamic and highly disturbed environment, and the prefabricated trajectory is relatively stable, when the GNSS signal fails, the measurement update of the filter driven by the prefabricated trajectory instead of the GNSS signal will inevitably have abnormal data at certain time points. The fixed measurement noise setting may cause the filter to be less accurate or unstable. Therefore, based on the idea of ​​robust estimation, an adaptive measurement noise covariance gain coefficient is introduced into the traditional Kalman filter. By changing the measurement noise covariance matrix, the filter gain can be adjusted in real time, the impact of abnormal data on the filtering results can be reduced, and the system robustness can be improved.

[0116] The improved robust Kalman part is as follows:

[0117]

[0118] The abnormality of the measured data can also be reflected in the correlation between the prefabricated trajectory and the real trajectory. After the best prefabricated trajectory is selected, the system will continue to solve its df i,k , as the adaptive outer loop parameter in the filter structure that is connected to the external measurement, and with the help of this value, the measurement noise covariance gain coefficient at time k is constructed The robust inner loop adjustment parameters of the filter are formed and can be expressed as

[0119]

[0120] Wherein, η>0 and κ>0 are parameters to be designed, and this application sets η=1, κ=1.3. df i,k The adaptive measurement noise covariance gain coefficient described in formula (25) is According to df i,k Adaptive adjustment capability: hour, Drive R k Rapidly increase, making K k Rapidly decreases, achieving rapid adjustment of KF filter gain; when hour, Keep it unchanged at 1. Finally, df i,k The mean stabilizes at 1.

[0121] Therefore, the calculation steps of the adaptive robust modified Kalman filter are as follows:

[0122]

[0123]

[0124] Experiment and verification

[0125] The projectile trajectory position error, position root mean square error (RMSE), and trajectory space position error are used as evaluation indicators. The projectile trajectory space error is defined as follows:

[0126]

[0127] In the formula, and are the estimated east, north and sky positions at time k of the proposed method, r E.k 、r N,k and r U,k They are respectively the east, north and sky positions at time k of the real trajectory.

[0128] In the comparative experiment, this application intends to compare the results of the proposed method (abbreviated as ReSet-ARKF) with the inertial navigation (abbreviated as INS), INS and non-corrected prefabricated trajectory KF filter fusion method (abbreviated as Set-KF), and INS and corrected prefabricated trajectory KF filter fusion method (abbreviated as ReSet-KF).

[0129] The simulation experiment will be described in detail below.

[0130] 1) Simulation environment

[0131] This application uses a 155mm large-scale differential-spin guided artillery shell as the simulation object. The GNSS signal is valid for the first 12 seconds, and a seven-degree-of-freedom rigid body trajectory model is used to output the projectile flight data for algorithm verification. This application sets the initial firing direction of the prefabricated trajectory set on the ground before launch to due north, the initial firing angle is 30°, the firing speed is 611m / s, the sampling frequency is 1000Hz, the launch point coordinates are (126°, 45°, 0m), and the wind speed is 0m / s; the real trajectory is set to be unchanged except for the wind speed changed to 2.5m / s; at the same time, a prefabricated trajectory with a wind speed interval of 1m / s to 10m / s is generated for screening.

[0132] The sensor parameters used in the simulation and experiment are shown in Table 1. The parameter settings in the weight matrix Π in the multi-prefabricated trajectory correlation analysis process are shown in Table 2.

[0133] Table 1 Sensor parameters

[0134]

[0135] Table 2 Weight matrix Π

[0136]

[0137] 2) Simulation results and analysis

[0138] When the GNSS signal is valid for the first 12 seconds, the statistical distances df between the prefabricated trajectory and the GNSS positioning information at different wind speeds and the estimated trajectory are different. It can be seen from the above that during the period when the GNSS signal is valid, the prefabricated trajectories corresponding to the crosswind speeds of 2m / s and 3m / s meet the threshold rule, and the statistical distance root mean square of the wind speed of 2m / s is about 0.2, and the root mean square of the wind speed of 3m / s is about 0.4, so according to the screening rule, the prefabricated trajectory corresponding to the crosswind speed of 2m / s is selected for the subsequent fusion filtering process.

[0139] From the above experiments, it can be seen that the Set-KF method is better than the INS method. The root mean square errors of the east, north, and celestial positions under the INS method are 530m, 170m, and 35m respectively, and the spatial error is 560m on average. The root mean square errors of the east, north, and celestial positions under the Set-KF method are 29.2m, 1.7m, and 1.5m respectively, and the root mean square error of the spatial position is 29.3m, indicating that the proposed method of the present application to provide more accurate navigation information for the projectile by assisting INS with a prefabricated trajectory when the GNSS signal fails is feasible. At the same time, it can also be seen from the above that the ReSet-KF method is better than the Set-Kf method. The root mean square errors of the east, north, and celestial positions under the ReSet-KF method are 7.6m, 1.57m, and 1.1m respectively, and the root mean square error of the spatial position is 7.84m, indicating that the present application corrects the yaw information of the prefabricated trajectory, which is also effective in improving the navigation accuracy of the projectile. In addition, it can be seen that the navigation result obtained by the ReSet-ARKF method is more accurate than that of the ReSet-KF method. The root mean square errors of the east, north, and celestial positions under the ReSet-ARKF method are 1.7m, 1.3m, and 1m respectively, and the root mean square error of the spatial position is 2.3m, indicating that the measurement noise covariance gain coefficient composed of nonlinear functions is Dynamically adjust the measurement noise covariance matrix when the prefabricated trajectory is used as the measurement information, thereby changing the filter gain K k It is feasible to improve the robustness and accuracy of the projectile navigation system filter. Therefore, the method proposed in this application is effective at the simulation level.

[0140] In order to verify the feasibility and accuracy of the method proposed in this application in the actual flight environment of guided artillery shells, a flight test was carried out with a certain type of guided artillery shell as the carrier, and the inertial sensor parameters are shown in Table 1. The guided artillery shell flight data measurement device integrates inertial sensors (gyroscopes, accelerometers), onboard computers, onboard data recorders, power supplies, pulse ignition control, pulse ignition charging, and satellite receivers, and is placed at the stern of the guided artillery shell in a multi-layered small tower structure. After the guided artillery shell lands, the data of the gyroscope, accelerometer, and satellite receiver during the flight of the projectile are read from the onboard data recorder to verify the method of this application.

[0141] The coordinates of the launch position of the guided projectile are (124.5°E, 47.5°N, 2m), the firing direction is due north, the initial firing angle is 30°, and the firing speed is about 609m / s. The weather conditions before the launch were good, and the crosswind speed was 0m / s, so the initial prefabricated trajectory was set. During the actual flight of the projectile, the GNSS signal is valid throughout the entire process and the position information (10Hz) provided by it is used as the real flight trajectory. However, during the navigation algorithm verification process, the information provided by the GNSS signal 12s after the projectile is launched is deleted. Before the projectile is launched, a prefabricated trajectory with a crosswind speed of 1m / s-10m / s is set (the other initial parameters except the wind speed are the same as the initial prefabricated trajectory) for subsequent screening.

[0142] The method proposed in this application can still provide relatively accurate position information for the guided projectile when it is affected by crosswind during flight and the GNSS signal is challenged. The position errors of several methods are compared. The root mean square errors of the east, north, and sky positions under the Set-KF method are 28.8m, 3.5m, and 3.3m respectively, and the root mean square error of the spatial position is 29.3m; the root mean square errors of the east, north, and sky positions under the ReSet-KF method are 9.2m, 1.9m, and 1.22m respectively, and the root mean square error of the spatial position is 9.5m; the root mean square errors of the east, north, and sky positions under the ReSet-ARKF method are 3m, 2m, and 1m respectively, and the root mean square error of the spatial position is 3.8m. It can be seen that the large-scale differential spin guided projectile navigation method based on multi-prefabricated trajectory constraints proposed in this application can provide relatively accurate positioning information for large-scale differential spin guided projectiles in an environment where crosswinds are affected and GNSS is challenged.

[0143] In view of the difficulty in effectively obtaining accurate positioning information in a high-dynamic environment, crosswind interference, and GNSS challenged complex environment during the launch of guided artillery shells under conventional systems, this application proposes a large-scale differential spin tail control missile navigation method under the constraints of multiple prefabricated ballistics. The reliability and accuracy of this method have been verified through simulation tests and flight tests.

[0144] The experimental results show that the spatial position root mean square error of the algorithm under simulation conditions is 2.3m; the spatial position root mean square error under flight test conditions is 3.8m, which effectively achieves the accurate acquisition of positioning data when the projectile is affected by crosswinds during flight and when the GNSS signal fails. The next step of this application is to use a neural network model based on physical information constraints to estimate the relevant ballistic information during the flight of the projectile in real time without satellite signals, and dynamically generate a highly reliable prefabricated trajectory as a more accurate observation to achieve accurate autonomous navigation of the guided artillery shell throughout the entire process.

[0145] The present application also provides a large-scale differential spin guided projectile navigation device under multiple prefabricated trajectory constraints, such as Fig.10 As shown, it includes: a GNSS-INS navigation module 12, which is configured to use the GNSS signal and INS data to navigate the guided artillery shell when the GNSS signal is available; an optimal prefabricated trajectory-INS navigation module 14, which is configured to use the correlation analysis results of the prefabricated trajectory and the estimated trajectory obtained when the GNSS signal is available to screen out the optimal prefabricated trajectory when the GNSS signal is not available, and navigate the guided artillery shell based on the optimal prefabricated trajectory and INS data.

[0146] It should be noted that the large-scale differential spin guided artillery shell navigation device under the constraints of multiple prefabricated trajectories provided in the above embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the large-scale differential spin guided artillery shell navigation device under the constraints of multiple prefabricated trajectories provided in the above embodiment and the large-scale differential spin guided artillery shell navigation method embodiment under the constraints of multiple prefabricated trajectories belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0147] Fig.11 FIG. 1 shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present disclosure. It should be noted that: Fig.11 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0148] like Fig.11As shown, the electronic device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0149] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0150] In particular, according to an embodiment of the present disclosure, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, various functions defined in the method and apparatus of the present application are executed. In some embodiments, the electronic device may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0151] It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0152] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0154] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device.

[0155] The computer-readable medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments. For example, the electronic device can implement each step of the method embodiment.

[0156] As another aspect, the present application further provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program executable by the at least one processor, and the computer program is used to enable the electronic device to perform the method of the embodiment of the present application when executed by the at least one processor.

[0157] As another aspect, the present application further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present application.

[0158] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0159] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0161] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0162] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0163] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0164] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A large-scale differential spin guided projectile navigation method under multiple prefabricated trajectory constraints, characterized in that: include: When GNSS signals are available, use GNSS signals and INS data to guide artillery shells; When the GNSS signal is unavailable, the best prefabricated trajectory is screened out by using the correlation analysis result between the prefabricated trajectory and the estimated trajectory obtained when the GNSS is available, and the guided projectile is navigated based on the best prefabricated trajectory and INS data.

2. The method according to claim 1, characterized in that: Use GNSS signals and INS data to guide artillery shells, including: Determine the first position and velocity information of the guided projectile based on the GNSS signal, and calculate the second position and velocity information of the guided projectile based on the INS data; generating a GNSS-INS measurement residual based on the first position and velocity information and the second position and velocity information; A KF filter is driven based on the GNSS-INS measurement residual to estimate the position, velocity and attitude information of the guided projectile.

3. The method according to claim 1, characterized in that: The method comprises: selecting the best prefabricated trajectory by using the correlation analysis result between the prefabricated trajectory and the estimated trajectory obtained when the GNSS is available, and navigating the guided projectile based on the best prefabricated trajectory and INS data, including: The best prefabricated trajectory is selected based on the correlation analysis results between the prefabricated trajectory and the estimated trajectory obtained when GNSS is available; Constructing a yaw information correction function based on the flight dynamic characteristics of the guided projectile, and using the yaw information correction function to correct the optimal prefabricated trajectory information; The residual is obtained by using the optimal prefabricated trajectory information corrected by the yaw information correction function and the position and velocity information solved by INS; The residual is used to drive the robust KF to estimate the position, velocity and attitude information of the guided projectile.

4. The method according to claim 3, characterized in that The yaw information correction function is constructed based on the flight dynamic characteristics of the guided projectile, including: constructing the yaw information correction function based on the lateral deviation position before and after correction in the prefabricated trajectory geographic coordinate system, the prefabricated trajectory yaw angle, and the estimated trajectory yaw angle.

5. The method according to claim 3, characterized in that: The robust KF is obtained based on the following: Calculating the statistical distance of the optimal prefabricated trajectory, and using the solved statistical distance as an adaptive outer loop parameter in the filter structure that is connected to the external measurement; The adaptive outer loop parameters are used to construct the measurement noise covariance gain coefficient at time k, and the measurement noise covariance gain coefficient is used as the robust inner loop adjustment parameter of the filter to obtain the robust KF.

6. The method according to claim 5, characterized in that The measurement noise covariance gain coefficient has the ability to adaptively adjust according to the statistical distance. When the statistical distance is greater than the product of the root mean square of the statistical distance and a preset constant coefficient, the measurement noise covariance gain coefficient drives the root mean square to increase rapidly, so that the filtering gain decreases rapidly; when the statistical distance is less than or equal to the product of the root mean square of the statistical distance and a preset constant coefficient, the measurement noise covariance gain coefficient remains unchanged at 1.

7. A large-scale differential spin guided projectile navigation device under multiple prefabricated trajectory constraints, characterized in that: include: A GNSS-INS navigation module configured to use GNSS signals and INS data to guide the projectile when GNSS signals are available; Best Prefabricated Trajectory - INS navigation module, configured to utilize The result of correlation analysis between the prefabricated trajectory and the estimated trajectory obtained when GNSS is available is used to screen out the best prefabricated trajectory, and navigate the guided projectile based on the best prefabricated trajectory and INS data.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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