Intelligent Decision-Making Algorithm for Multi-UAV Radar Cooperative Guided Search Task Based on Multi-Agent Reinforcement Learning

To address the multi-UAV radar cooperative guided search task in the scenarios of large airspace with widespread distribution of cluster targets, an autonomous hierarchical decision-making framework based on multi-agent reinforcement learning is proposed to guide different airborne radar search processes in cooperative search tasks. Firstly, the top-level and bottom-level strategy modules are constructed based on the cooperative airspace set-covering model and search performance optimization models established in the above scenario respectively. Secondly, a cooperative search environment based on real-time beam scheduling is constructed where a complementary scheduling strategy for cooperative radar search beam positions is proposed to minimize cooperative search time and maximize global radar search performance. Finally, the lamaddpg-rgs algorithm is proposed to perform long-short term memory (LSTM) and feature extraction on the variable global observation state sequence, aiming to improve the algorithm training effect and convergence speed. Simulation results show that the trained hierarchical decision-making multi-agents can make precise autonomous decisions rapidly based on local observation states and current search task process. The optimization effect of radar cooperative search performance in above scenario based on proposed algorithm is superior to traditional algorithms.