Terminal guidance law of small anti-ship missile based on DDPG

摘要

Intelligence of weapon system, precision of striking weapon and diversification of combat formulation are important developing trends in future combat operations. A terminal guidance law based on reinforcement learning (RL) and deep deterministic Policy Gradient (DDPG) is proposed to solve the problems in missile guidance system, such as heavy dependence on simulated environment training, poor interception effected by condition constraints, and insufficient guidance accuracy caused by the difference between simulated environment and the real environment. Taking advantage of the maneuverability of the small anti-ship missile relative to the ship, according to the game theory in reinforcement learning combined with the deep neural network, the optimizing trajectory is updated by analyzing the projectile motion and line of sight angle. Reward was set reasonably in line with the spatial position and the strategy gradient was optimized by using the deep neural network. Simulating experiments show that after iterative training, the DDPG guidance model for small anti-ship missiles can optimize the ballistic curve, and the miss distance can meet the requirements. Compared with traditional guidance laws, this model has better autonomous decision-making ability and strike capability.