Development of reinforcement learning based mission planning method for active off-board decoys on naval platforms

Abstract

In this paper, a reinforcement learning-based decoy deployment strategy is proposed to protect naval platforms against radar seeker-equipped anti-ship missiles. The decoy system consists of a rotary-wing unmanned aerial vehicle (UAV) and an integrated onboard jammer. This decoy concept enables agility which is quite critical for jamming operations against a high-speed anti-ship missile. There are two main purposes of the developed jamming strategy; a) flying in the field of view of the anti-ship missile to conceal the naval platform, and b) flying away from the target ship to increase the miss distance between the anti-ship missile and naval platform. Here, it is aimed to meet these requirements simultaneously. Kinematics models are used to represent missile, decoy UAV, and target motion. Jammer and seeker signal strengths are modeled and the radar-cross section of a frigate is utilized to increase the realism of the simulation environment. Deep Deterministic Policy Gradient (DDPG) algorithm is applied to train an actor-critic agent which maps the observation parameters to decoy’s lateral acceleration. A heuristic way is chosen to create an appropriate reward function to solve the decoy guidance problem. Finally, simulations studies are performed to evaluate the system performance