Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

Impact Point Prediction and Lateral Correction Analysis of Two-dimensional Trajectory Correction Projectiles

Compared with the one-dimensional trajectory correction technology which adjusts longitudinal range, not only does the two-dimensional trajectory correction technology adjust the force in velocity direction, but also need to modulate the lateral force or trajectory (perpendicular to the vertical plane of fire direction). Therefore, the structure of control cabin of two-dimensional trajectory correction projectile (TDTCP) is more complicated than that of one-dimensional trajectory correction projectile (ODTCP). To simplify the structure of control cabin of TDTCP and reduce the cost, a scheme of adding a damping disk to the control cabin of ODTCP has been developed recently. The damping disk is unfolded at the right moment during its flight to change the ballistic drift of spin stabilized projectile. For this technical scheme of TDTCP, a fast and accurate impact point prediction method based on extended Kalman filter is presented. An approximate formula for predicting the ballistic drift and trajectory correction quantity is deduced. And the lateral correction capability for different fire angles and its influencing factors are analyzed. All the work is valuable for further research.