Applying of aircraft decoys to counter air and missile defense: a multi-agent reinforcement learning approach

Recent advances in radar homing head technology have significantly improved the accuracy and effectiveness of interceptor missiles against aerial targets. This is especially critical in the context of protecting aircraft, where appropriate countermeasures are needed to reduce the likelihood of interception by hypothetical enemy missile defense systems. This article presents a reinforcement learning–based strategy for deploying decoys to enhance the probability of aviation strike assets penetrating hostile air and missile defense systems, as well as to improve their survivability during engagement. The strategy involves coordinated actions of decoys trained using MADDPG (Multi-Agent Deep Deterministic Policy Gradient) and MATD3 (Multi-Agent Twin Delayed Deep Deterministic Policy Gradient) algorithms. The decoys operate under a leader–follower architecture, employing various formation patterns to ensure effective intra-group coordination. The approach enables dynamic situation assessment across multiple parameters, including deployment zones of decoys, interceptor missile launch vectors, and the maximum velocities of both decoys and interceptor missiles operated by the hypothetical adversary’s integrated air and missile defense systems.