# Adaptive ECCM for Mitigating Smart Jammers

arXiv: https://arxiv.org/abs/2212.02002 · HTML: https://mlsanity.com/paper/2212.02002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference](https://mlsanity.com/paper/2008.01559/) (2020)
- [Online Learning and Profit Maximization from Revealed Preferences](https://mlsanity.com/paper/1407.7294/) (2014)
- [How can a Cognitive Radar Mask its Cognition?](https://mlsanity.com/paper/2110.08608/) (2021)
- [Principal Agent Problem as a Principled Approach to Electronic Counter-Countermeasures in Radar](https://mlsanity.com/paper/2109.03546/) (2021)
- [How can a Radar Mask its Cognition?](https://mlsanity.com/paper/2210.11444/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments](https://mlsanity.com/paper/2405.20642/) (2024)

## Abstract (arXiv)

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.