Optimization Technology for Intelligent Interception of Incoming Missiles and Platform Maneuvering Strategies Based on Deep Reinforcement Learning

Facing the increasing complexity of aerial combat environments and challenges to the survivability of air platforms from new combat methods, it is necessary to adopt new hard-kill methods to counter advanced air-to-air missiles. In order to improve the success rate and efficiency of launching air-to-air missiles to intercept incoming missiles as a hard kill method, this study proposes intelligent maneuvering strategies for aircraft platforms and missile interception strategies based on reinforcement learning. Firstly, this paper designs the missile threat assessment technology, constructs the simulation environments, and determines the strategy model state and reward function. By setting various attack angles and positions of incoming air-to-air missiles and training maneuvering and intelligent interception strategies under different aircraft platform postures, this paper achieves active interception of incoming targets and effective maneuvering of the aircraft platform. Experiments show that compared to the average escape probability of 5.8% in operations research game strategies, after using maneuver and interception strategies based on reinforcement learning, the average escape probability can increase to 56.8%; Meanwhile, the utilization rate of interceptors has increased by approximately 13.3%, and the response time has remained within 24 ms. The designed strategy can adapt to different numbers of incoming missiles, can significantly improve the survival ability of the carrier platform and the success rate of intercepting incoming missiles. This study can support continuous optimization in a high-dimensional state space of air combat.