A prediction and correction reentry guidance method based on BP network and deep Q-learning network

A prediction and correction reentry guidance method based on the BP network and the deep Q-learning network (DQN) is proposed to address the issues of low computational efficiency and difficulty in the online application of a traditional numerical prediction and correction guidance algorithm. This method adopts the design concept of longitudinal and lateral guidance decoupling. For longitudinal guidance, a residual range prediction BP network is constructed and trained, and the predicted range deviation is used to correct the pitch angle profile parameters. For lateral guidance, firstly, the state and action space needed by the reinforcement learning are constructed to solve re-entry guidance problems. Secondly, the decision points are determined and the reward function that considers comprehensive performance is designed. The reinforcement learning training network is constructed to achieve tilt reversal decision-making through the learning network. Simulations are carried out with the CAV-H reentry gliding as example. The simulation results show that compared with the traditional numerical prediction and correction method, the longitudinal guidance method based on the BP network is significantly superior in terminal accuracy and computational efficiency. Compared with the traditional lateral guidance method based on the heading angle corridor, the lateral guidance method based on the DQN has considerable computational accuracy and fewer reversal times.