Adaptive Electromagnetic Working Mode Decision-Making Algorithm for Miniaturized Radar Systems in Complex Electromagnetic Environments: An Improved Soft Actor–Critic Algorithm

With the advancement of multi-function radar (MFR) technology, miniaturized radar systems (MRSs) inevitably operate in complex electromagnetic environments (CEEs) dominated by MFRs as single-function radars are gradually being replaced by MFRs. MFRs can not only flexibly switch working states and generate diverse radar signal characteristics, but they can also acquire the MRSs’ position information, which has a significant impact on the execution of the MRSs’ close-range remote sensing missions. For resource-constrained MRS, selecting the optimal electromagnetic working mode in such environments becomes a critical challenge. This paper addresses the adaptive electromagnetic working mode decision-making (EWMDM) problem for MRS in CEE by establishing an EWMDM model and proposing a reinforcement learning (RL) method based on an improved soft actor–critic algorithm with prioritized experience replay (SAC-PER). First, we simulate the process of MRS receiving pulse description words (PDWs) from MFR waveforms and introduce noise into the PDWs to emulate real electromagnetic environments. Then we use a threshold to filter out uncertain recognition results to reduce the impact of noise on the MFR’s working state recognition. Subsequently, we analyze the limitations of the SAC-PER algorithm in noisy environments and propose an improved algorithm—SAC with alpha decay prioritized experience replay (SAC-ADPER)—to address the influence of environmental noise and stochasticity. Experimental results show that SAC-ADPER significantly accelerates the convergence speed of EWMDM in noisy environments and validate the effectiveness of the proposed method.