Radar active antagonism through deep reinforcement learning: A Way to address the challenge of mainlobe jamming

Abstract

Among different jamming techniques, mainlobe jamming is difficult to deal with for the radar and traditional passive anti-jamming methods are less effective because the angular separation between the jammer and the target is almost the same. In contrast to these passive methods, active antagonism requires the radar to take measures in advance to avoid being jammed and this can be achieved via frequency agile (FA) radar. In order to enable the FA radar to combat the jammer and obtain good performance, a deep reinforcement learning (RL) based anti-jamming strategy design method is proposed in which a transmit/receive time-sharing jammer may adopt multiple different jamming strategies. To combat the individual jamming strategy, we propose a specialized strategy learning algorithm that treats probability of detection as the reward signal and uses proximal policy optimization to solve the RL problem of the radar and the jammer. Based on the learned specialized strategies, policy distillation technique is applied to design a unified strategy which enables the FA radar to combat multiple jamming strategies. Simulation results show that the FA radar can avoid being jammed and obtain a high probability of detection whether the jammer adopts individual or multiple jamming strategies through the proposed method.