mlsanityData to 14 Oct 2026

Adaptive ECCM for Mitigating Smart Jammers

arXiv 2212.02002 ↗First posted 5 Dec 2022eess.SP cs.LGcited by 1

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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.

Key facts. Cited by 1 paper 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

5 of 23 references.

References · 5

  1. 2020Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interferencecited by 10
  2. 2014Online Learning and Profit Maximization from Revealed Preferencescited by 6
  3. 2021How can a Cognitive Radar Mask its Cognition?cited by 4
  4. 2021Principal Agent Problem as a Principled Approach to Electronic Counter-Countermeasures in Radarcited by 1
  5. 2022How can a Radar Mask its Cognition?cited by 1

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Momentscited by 0

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  1. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
  2. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  3. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences
  4. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars
  5. 2025Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
  6. 2024Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
  7. 2025Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks
  8. 2025How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning