| Summary: | 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 single-chip microcomputer, a single IPP takes about 2.374 ms, and a single guidance solution takes about 9.936 ms, which has a good real-time performance and a certain engineering application value.
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