  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.

Signal Processing;Machine Learning