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Design of Anti‐jamming Decision‐making for Cognitive Radar

IET RADAR SONAR AND NAVIGATION(2024)

Xidian Univ

Cited 0|Views9
Abstract
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. This work proposes an intelligent anti-jamming decision-making scheme designed for the cognitive jamming to an anti-jamming interaction mode. This scheme is suitable for scenarios with varying jamming types and the capability to predict jamming behaviour.image
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Key words
cognitive radio,decision making,jamming,markov processes,radar,radar signal processing,radiofrequency interference
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要点】:本文提出了一种基于强化学习的认知雷达抗干扰决策方案,创新性地将抗干扰措施视为一种交互行为,并建立了适用于抗干扰决策的认知雷达对抗环境模型。

方法】:研究采用了强化学习算法来解决雷达对抗环境下的抗干扰决策问题。

实验】:通过对不同强化学习算法的性能比较,验证了将强化学习理论应用于雷达对抗环境模型中进行抗干扰决策的可行性,并讨论了这些算法的优缺点。实验结果显示,该方案能够在多变的山杂干扰类型场景中预测干扰行为,展现了其在预测干扰行为方面的优势。