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.

在不同的干扰技术中，雷达很难处理主瓣干扰，而传统的被动抗干扰方法效果不佳，因为干扰器与目标之间的角度间隔几乎相同。与这些被动方法相比，主动对抗要求雷达提前采取措施避免被干扰，这可以通过频率捷变（FA）雷达来实现。为了使FA雷达能够抵抗干扰并获得良好的性能，提出了一种基于深度强化学习（RL）的抗干扰策略设计方法，其中发射/接收分时干扰器可以采用多种不同的干扰策略。为了对抗个人干扰策略，我们提出了一种专门的策略学习算法，该算法将检测概率作为奖励信号，并使用近端策略优化来解决雷达和干扰器的RL问题。基于所学的专业策略，运用策略提炼技术设计出统一的策略，使FA雷达能够应对多种干扰策略。仿真结果表明，通过该方法，FA雷达可以避免被干扰，并具有较高的检测概率，可以确定干扰器是采用单个干扰策略还是采用多种干扰策略。