Abstract: 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 hinder their ability to meet the precise collision requirements of traditional proportional navigation guidance, posing new challenges for trajectory prediction of incoming missiles. This paper addresses 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. A trajectory prediction method for incoming missiles based on inverse reinforcement learning is proposed. First, a mathematical model is constructed to extract the temporal maneuvering characteristics of incoming missiles under maximum causal entropy, and a be-havioral 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 func-tion 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 the proposed trajectory prediction network algorithm exhibits strong generalization capability in "out-of-model-set/sample-set" scenarios within the three-body active de-fense context. It shows good dynamic adaptability to complex target maneuvers and high prediction accuracy, provid-ing a high-probability trajectory prediction model usable for guidance in defending against incoming missiles. This work holds theoretical significance and offers valuable insights for engineering applications.

Key words: Three-Body Active Defense Scenario, Guided missiles, Active defense, Inverse reinforcement learning, Trajectory prediction