Adaptive ECCM for Mitigating Smart Jammers

TL;DR

This paper introduces an adaptive ECCM method for radar systems that models jammer interactions as a Principal Agent Problem, using inverse reinforcement learning to identify and mitigate jammer utility over time.

Contribution

It presents a novel adaptive ECCM approach that employs economic theories and deep learning to learn and counteract jammer strategies in real-time.

Findings

Successfully identifies jammer utility over time

Effectively mitigates jammer interference

Demonstrates improved radar resilience

Abstract

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.