  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.
