Learning-Based Multi-Stage Strategy for a Fixed-Wing Aircraft to Evade a Missile Detected at a Short Distance
Zhiguan Niu, Xiaochao Zhou, and Hao Xiong

TL;DR
This paper introduces a multi-stage reinforcement learning strategy for fixed-wing aircraft to evade missiles detected at close range, outperforming traditional methods in high-fidelity simulations.
Contribution
It proposes a novel multi-stage RL-based evasion strategy inspired by pursuit-evasion games, with adaptive policies activated based on distance and azimuth.
Findings
Achieves 80.89% success rate in missile evasion across various conditions.
Outperforms baseline strategies in simulated high-fidelity environment.
Success rate increases to 85.06% when missile detection occurs beyond 8000 m.
Abstract
Missiles pose a major threat to aircraft in modern air combat. Advances in technology make them increasingly difficult to detect until they are close to the target and highly resistant to jamming. The evasion maneuver is the last line of defense for an aircraft. However, conventional rule-based evasion strategies are limited by computational demands and aerodynamic constraints, and existing learning-based approaches remain unconvincing for manned aircraft against modern missiles. To enhance aircraft survivability, this study investigates missile evasion inspired by the pursuit-evasion game between a gazelle and a cheetah and proposes a multi-stage reinforcement learning-based evasion strategy. The strategy learns a large azimuth policy to turn to evade, a small azimuth policy to keep moving away, and a short distance policy to perform agile aggressive maneuvers to avoid. One of the…
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Taxonomy
TopicsGuidance and Control Systems · Military Defense Systems Analysis · Spacecraft Dynamics and Control
