Robust Reinforcement Learning-based Wald-type Detector for Massive MIMO Radar

The two basic performance indices characterizing the multi-target detection task in a radar system are the probability of false alarm (P_FA) and the probability of detection P_D. It is well-known that, when the disturbance model (i.e., clutter and noise) is perfectly known, the Neyman-Pearson (NP) detector provides the best decision strategy, i.e., the detector that maximizes the P_D, while keeping a constant P_FA. However, in practical scenarios, the a priori knowledge of the statistical model of the disturbance is rarely available. In this paper we investigate the robustness of a reinforcement learning (RL) based Wald-type test to guarantee reliable detection performance even without knowledge of the disturbance distribution. Specifically, the constant false alarm Rate (CFAR) property is obtained by applying tools from misspecified asymptotic statistics, while the P_D is maximized by exploiting an RL-based scheme.