mlsanityData to 14 Oct 2026

How can a Radar Mask its Cognition?

arXiv 2210.11444 ↗First posted 20 Oct 2022eess.SP cs.LGcited by 1

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Abstract (arXiv)

A cognitive radar is a constrained utility maximizer that adapts its sensing mode in response to a changing environment. If an adversary can estimate the utility function of a cognitive radar, it can determine the radar's sensing strategy and mitigate the radar performance via electronic countermeasures (ECM). This paper discusses how a cognitive radar can em hide its strategy from an adversary that detects cognition. The radar does so by transmitting purposefully designed sub-optimal responses to spoof the adversary's Neyman-Pearson detector. We provide theoretical guarantees by ensuring the Type-I error probability of the adversary's detector exceeds a pre-defined level for a specified tolerance on the radar's performance loss. We illustrate our cognition masking scheme via numerical examples involving waveform adaptation and beam allocation. We show that small purposeful deviations from the optimal strategy of the radar confuse the adversary by significant amounts, thereby masking the radar's cognition. Our approach uses novel ideas from revealed preference in microeconomics and adversarial inverse reinforcement learning. Our proposed algorithms provide a principled approach for system-level electronic counter-countermeasures (ECCM) to mask the radar's cognition, i.e., hide the radar's strategy from an adversary. We also provide performance bounds for our cognition masking scheme when the adversary has misspecified measurements of the radar's response.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 61 references that are in the map. First posted on arXiv 20 Oct 2022.

Builds on

9 of 61 references.

References · 9

  1. 2021Convex optimizationcited by 2,515
  2. 2016Partially Observed Markov Decision Processes. Problem Sets and Internet Supplementcited by 34
  3. 2015Watch and Learn: Optimizing from Revealed Preferences Feedbackcited by 17
  4. 2019Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferencescited by 13
  5. 2020Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interferencecited by 10
  6. 2014Online Learning and Profit Maximization from Revealed Preferencescited by 6
  7. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radarscited by 6
  8. 2021How can a Cognitive Radar Mask its Cognition?cited by 4
  9. 2022Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learnercited by 3

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Citing papers per year: 2022: 1.

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  1. 2025A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning
  2. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
  3. 2024Online waveform selection for cognitive radar
  4. 2025Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
  5. 2026Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
  6. 2025Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
  7. 2026Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar
  8. 2025RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs