Swarm decoys deployment for missile deceive using multi-agent reinforcement learning - Bildik, Enver; Tsourdos, Antonios; Perrusquía, Adolfo; Inalhan, Gokhan (Institute of Electrical and Electronics Engineers (IEEE), 2024)
The development of novel radar seeker technologies has improved the hit-to-kill capability of missiles. This is particularly worrying in safety and security domains that need the design of appropriate countermeasures against adversarial missiles to ensure protection of naval facilities. This paper aims to contribute in these domains by developing an artificial intelligence (AI) based decoy deployment system capable of deceiving the missile threat. Here, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is developed to maximise the distance between the target and the missile by learning the optimal/near optimal route planning of the six decoys to reach the global mission. As case study, the deployment of six decoys from the top deck of the main platform is assumed. The decoys are launched from the platform at the initial phase of the mission, and they establish a leader-follower formation that enhances the signal strength of the swarm decoys. The reward function is designed to guarantee a triangular formation configuration for swarm decoys. The reported results show that the proposed approach is capable to deceive the missile threat and has the potential to be integrated in current naval platforms.
Keywords: Missiles; Reinforcement learning; Radar; Radar countermeasures; Planning; Security; Protection