Multi-Antenna Phased Array Radar-Guided Search Resource Optimization Algorithm Based on MADDPG

An optimization algorithm for multi-antenna phased array radar search parameters based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed to address the problem that search parameters of the traditional single-antenna phased array radar optimization model are difficult to solve for in complex multi-antenna phased array scenarios. First, considering the constraints of a multi-antenna phased array scenario and the actual search task requirements of airborne radar, an optimization model is established for multi-antenna radar search parameters based on the maximum accumulated discovery probability of cluster targets. Second, the local and global observation spaces of multi-agents and composite reward functions with discount factors are designed to update the allocation coefficients of search resources online for each antenna phased array and each agent strategy network based on the Actor-Critic structure. Finally, the simulation results show that the trained multi-agents of the proposed algorithm can quickly make accurate autonomous decisions based on the target airspace set-covering model and target guidance information. The performance of the proposed algorithm is significantly better than that of the traditional algorithm in the multi-antenna phased array scenario.