Adaptive ECCM for Mitigating Smart Jammers
This addresses the challenge of electronic counter-counter measures for radar systems against smart jammers, representing an incremental improvement by applying economic frameworks to a domain-specific problem.
This paper tackles the problem of mitigating adversarial jammers in radar systems by modeling the jammer-radar interaction as a Principal Agent Problem and using inverse reinforcement learning to adaptively learn the jammer's utility, with numerical results showing successful identification and mitigation over time.
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.