关键词: 机动目标, 逆轨拦截, 弹道预测, 预测命中点, 智能制导律

Abstract:

To achieve the maximum interception terminal velocity when intercepting maneuvering targets in the contra-orbit， this paper constructs a target maneuvering ballistic branch prediction model based on the sequence-to-sequence method， and constructs a deep reinforcement learning intelligent guidance law based on the deep Q-learning algorithm and the bias guidance law. To address the sparse reward problem caused by the training process of the smart guidance law， the prediction-correction method is used to introduce the guidance ratio of the terminal guidance to construct the terminal reward， and the process reward is constructed by combining the physical process and performance indexes. The process reward and terminal reward are combined to improve the training effect. Simulation shows that the target maneuvering ballistic branch prediction model improves the average prediction accuracy of the maneuvering ballistic in the sub-direction by at least 67% compared with the ballistic extrapolation method， and the intelligent guidance law improves the relative interception speed by 67% compared with the baseline guidance law on the premise of meeting the requirements of shift performance and low overload.

Key words: maneuvering target, head-on interception, trajectory prediction, predicted hit point, intelligent guidance law