Reinforcement Learning-based Multi-Target Detection Method for MIMO Radar via Multi-rank Beamformer

Yongxiang Liu

Abstract: Currently, reinforcement learning (RL) has been applied for the multi-target detection task of MIMO radar. However, the existing methods still have two shortcomings: 1) the detection performance on weak targets is insufficient, and 2) the solving time of beam optimization scheme (BOS) is long. For the first issue, this paper first proposes a partially time-varying statistic vector, which can more robustly distinguish the target cell from clutter cell during the “action” step of RL. Secondly, considering the scenario where both strong and weak targets exist, a BOS with strong target limitation is proposed, which aims to limit the power gain of beampattern on strong targets. Thirdly, considering the scenario with all weak targets, the search mode of radar is given, and its corresponding BOS is designed. The aim is to search for possible weak targets by raising the power gain within a part of observation area. For the second issue, by introducing multi-rank beamformer, the existing and proposed BOSs are transformed into corresponding convex minimax constraint optimization problems, and their closed-form solutions are derived. Accordingly, a fast solution method is proposed. By combining the above improvements with the Markov decision process model of MIMO radar, as well as the quasi E-greedy policy and the optimized reward mechanism in existing methods, the proposed method is given. Sufficient experiments verify that the proposed improvements are valid, and the proposed method owns better detection performance compared with its competitors.