Research on Autonomous Maneuvering Strategies of UAV Swarm for Saturation Attack

In order to solve the problem of unmanned aerial Vehicle (UAV) cluster saturation attack, the autonomous maneuver strategy of UAV is studied. Based on the distributed partial observable Markov decision process, the autonomous maneuver decision model of UAV is constructed, and the Recurrent Multi-Agent Deep Deterministic Policy Gradient (RMADDPG) is used to learn the maneuver strategy of UAV cluster saturation attack. Combined with the typical characteristics of saturation attack scenario, two reward functions of global reward and local reward for UAV maneuver are designed. The trained UAVs can effectively perform suicide saturation attack task. According to the mission target design evaluation index of saturation attack, the Monte Carlo analysis method is used to compare the proposed method with particle swarm optimization (PSO) algorithm in three aspects: inter aircraft collision avoidance, NFZ avoidance and simultaneous attack. The experimental results show that the trained UAVs can effectively perform the suicide saturation attack task under the condition of a small amount of communication, which provides a method for the application of multi-agent deep reinforcement learning to UAV cluster operations.