再入滑翔飞行器俯冲段智能博弈机动策略

Aiming at the problem that the reentry glide vehicle in the dive phase is threatened by interceptors, an intelligent game-maneuvering strategy is proposed, which can effectively avoid the interceptor and ensure the impact accuracy. Firstly, a reinforcement learning-required training environment including dynamic, kinematic, and guidance law models of reentry glide vehicles and typical interceptors is constructed. Secondly, taking motion parameters as the state variables, the changing rates of attack of angle and bank angle are designed as the action variables, and the reward function is designed based on the miss distance of the interceptor and the impact deviation. Thus, the game-maneuvering problem is transformed into a Markov decision process. In order to solve the problem of sparse rewards in deep reinforcement learning, the sampling probabilities of higher priority samples and successful samples are improved to accelerate strategy promotion. Meanwhile, an adaptive action noise based on task success rate is designed to improve the exploration mechanism of the deep deterministic policy gradient algorithm and improve the training efficiency. Finally, mathematical simulations are performed for typical scenarios, which imply that the task-success rate in generation scenarios of the proposed intelligent game-maneuvering policy is greater than 90%, thereby verifying the validity of the method and the generation ability of neural networks.