Trajectory prediction method of incoming missiles based on improved inverse reinforcement learning in aircraft active defense mode

With the advancement of aircraft fire control systems and situational awareness capabilities, defense strategies against air-to-air missiles are evolving from passive methods such as jamming and deception to active defense modes involving interceptor missiles countering incoming threats. However, the low average velocity, limited defense space, and insufficient overload ratio of interceptor missiles make it difficult for traditional proportional navigation guidance to meet the precise collision requirements, posing new challenges for trajectory prediction of incoming missiles. To achieve the high-probability prediction of guidance information for interceptor missiles in a three-body active defense scenario involving the carrier aircraft, incoming missile, and interceptor missile, this paper provides an incoming missile trajectory prediction method based on inverse reinforcement learning. First, a mathematical model is constructed to extract the temporal maneuvering characteristics of incoming missiles under the principle of maximum causal entropy, and a behavioral strategy library for the guidance law of incoming missiles is established within the inverse reinforcement learning framework. Then, a quadratic-based calculation formula for the inverse reinforcement learning strategy function is derived, reducing the computational complexity of the strategy function in high-dimensional states. Finally, the weighting coefficients of the strategy function are computed online using rolling window measurement data, enabling real-time optimization and adaptive weighted trajectory prediction distribution to form a real-time prediction model for incoming missile trajectories. Simulation results demonstrate that in the three-body active defense context, the proposed trajectory prediction network algorithm exhibits strong generalization capability in “out-of-model-set/sample-set” scenarios, good dynamic adaptability to complex target maneuvers, and high prediction accuracy. The method provides a high-probability trajectory prediction model suitable for guidance in defense, and thus has notable theoretical significance and engineering application value.