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

Autor:	Liu, Liangqi, Pu, Wenqiang, Li, Yingru, Jiu, Bo, Luo, Zhi-Quan
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by SAM2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.16274 Zobrazit plný text záznamu View this record from Arxiv