arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.16274eess.SP
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基于领域知识增强的在线凸优化的雷达抗干扰策略学习

Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization

Liangqi Liu, Wenqiang Pu, Yingru Li, Bo Jiu, Zhi-Quan Luo

AI总结:

针对电子战中雷达抗干扰现有方法样本效率低的问题,提出两种结合干扰机特性的领域知识增强OCO算法,理论与仿真均验证其性能优于基准,具备实际应用潜力。

摘要:

雷达与干扰系统之间的动态对抗是现代电子战(EW)面临的重大挑战,当前的主动学习方法仍存在样本效率不足、未能利用干扰机特性的问题。本文研究了频率捷变雷达与基于数字射频存储(DRFM)的智能干扰机之间的对抗场景,引入了在线凸优化(OCO,一种在线决策优化框架)来刻画这种对抗交互。值得注意的是,传统OCO算法由于每轮获取的信息有限,样本效率欠佳。为解决这一局限,本文提出两种改进算法,采用利用干扰机系统独特属性的无偏梯度估计器,并给出了静态遗憾与通用遗憾均为次线性的理论结果,标志着OCO性能的显著提升。此外,仿真结果表明,所提算法性能优于常用的OCO基准算法,具备在实际场景中有效部署的潜力。

英文摘要:

The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this paper, the competition between a frequency agile radar and a Digital Radio Frequency Memory (DRFM)-based intelligent jammer is considered. We introduce an Online Convex Optimization (OCO) framework designed to illustrate this adversarial interaction. Notably, traditional OCO algorithms exhibit suboptimal sample efficiency due to the limited information obtained per round. To address the limitations, two refined algorithms are proposed, utilizing unbiased gradient estimators that leverage the unique attributes of the jammer system. Sub-linear theoretical results on both static regret and universal regret are provided, marking a significant improvement in OCO performance. Furthermore, simulation results reveal that the proposed algorithms outperform common OCO baselines, suggesting the potential for effective deployment in real-world scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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