频率捷变雷达抗干扰对策的最小化虚拟遗憾法
Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar
摘要：
雷达与干扰机之间的竞争是现代电子战中的一个新兴问题，原则上可以看作是两个参与者的非合作博弈。在这项工作中，频率捷变（FA）雷达和噪声调制干扰机之间的竞争被认为是。由于现代FA雷达采用多脉冲相干处理，因此竞争是以多轮方式进行的，其中每个脉冲可以被建模为雷达与干扰机之间的一轮交互。为了捕获这种多轮属性以及游戏内的不完美信息，即，雷达和干扰机无法知道即将到来的信号，我们提出了一个广泛的形式的游戏制定这样的竞争。由于博弈信息状态的数量相对于脉冲的数量呈指数增长，因此找到纳什均衡（NE）策略可能是计算上难以处理的任务。为了有效地解决这个游戏，使用了一种基于学习的算法，称为深度反事实遗憾最小化（CFR）。数值仿真表明了深度CFR算法在近似寻找NE和获得最佳响应策略方面的有效性。
英文摘要：
The competition between radar and jammer is one emerging issue in modern electronic warfare, which in principle can be viewed as a non-cooperative game with two players. In this work, the competition between a frequency agile (FA) radar and a noise-modulated jammer is considered. As modern FA radar adopts coherent processing with several pulses, the competition is hence in a multiple-round way where each pulse can be modeled as one round interaction between the radar and jammer. To capture such multiple-round property as well as imperfect information inside the game, i.e., radar and jammer are unable to know the upcoming signal, we propose an extensive-form game formulation for such competition. Since the number of game information states grows exponentially with respect to number of pulses, finding Nash Equilibrium (NE) strategies may be a computationally intractable task. To effectively solve the game, a learning-based algorithm called deep Counterfactual Regret Minimization (CFR) is utilized. Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.
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