论文标题
用于缓解智能干扰者的自适应ECC
Adaptive ECCM for Mitigating Smart Jammers
论文作者
论文摘要
本文考虑了自适应雷达电子反击措施（ECCM），以通过对抗性干扰器来减轻ECM。我们的ECCM方法将Jammer-Radar的相互作用作为主要代理问题（PAP）建模，这是一个流行的经济学框架，用于两个具有信息失衡的实体之间的相互作用。在我们的设置中，雷达不知道干扰器的实用程序。取而代之的是，雷达使用逆增强学习随着时间的推移而自适应地学习干扰器的实用性。雷达的自适应ECCM目标是通过解决PAP来最大化其效用的两倍（1），（2）通过观察其响应来估计干扰器的效用。我们的自适应ECCM计划在合同理论中使用了对微观经济学和主要代理问题的偏爱的深刻思想。我们的数值结果表明，随着时间的流逝，我们的自适应ECM既可以识别并减轻干扰器的效用。
This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer. Our ECCM approach models the jammer-radar interaction as a Principal Agent Problem (PAP), a popular economics framework for interaction between two entities with an information imbalance. In our setup, the radar does not know the jammer's utility. Instead, the radar learns the jammer's utility adaptively over time using inverse reinforcement learning. The radar's adaptive ECCM objective is two-fold (1) maximize its utility by solving the PAP, and (2) estimate the jammer's utility by observing its response. Our adaptive ECCM scheme uses deep ideas from revealed preference in micro-economics and principal agent problem in contract theory. Our numerical results show that, over time, our adaptive ECCM both identifies and mitigates the jammer's utility.
