With the development of electronic warfare, anti-jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti-jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti-jamming decision-making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti-jamming decision-making. This article regards the anti-jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti-jamming decision-making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti-jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

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认知雷达抗干扰决策设计

随着电子战的发展，抗干扰措施变得越来越复杂。关于干扰策略已有一定的研究成果，但关于抗干扰策略的研究资料却很少。难以模拟真实的干扰环境，也没有合适的抗干扰决策模型可供研究。认知雷达可以感知环境并接收反馈，这为解决抗干扰决策问题提供了可能。本文将抗干扰措施视为一种交互行为，建立认知雷达对抗环境模型，利用强化学习算法解决抗干扰决策问题。最后，本文验证了在雷达对抗环境模型中应用强化学习理论进行抗干扰决策的可行性。比较了不同强化学习算法的性能，并讨论了它们的优缺点。