mlsanityData to 7 Oct 2026

Identifying Cognitive Radars -- Inverse Reinforcement Learning using Revealed Preferences

arXiv 1912.00331 ↗First posted 1 Dec 2019eess.SP cs.LGcited by 13

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

We consider an inverse reinforcement learning problem involving us versus an enemy radar equipped with a Bayesian tracker. By observing the emissions of the enemy radar,how can we identify if the radar is cognitive (constrained utility maximizer)? Given the observed sequence of actions taken by the enemy's radar, we consider three problems: (i) Are the enemy radar's actions (waveform choice, beam scheduling) consistent with constrained utility maximization? If so how can we estimate the cognitive radar's utility function that is consistent with its actions. We formulate and solve the problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and the algebraic Riccati equation. (ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar's actions in noise or the radar observes our probe signal in noise? We propose a statistical detector with a tight Type-II error bound. (iii) How can we optimally probe (interrogate) the enemy's radar by choosing our state to minimize the Type-II error of detecting if the radar is deploying an economic rational strategy, subject to a constraint on the Type-I detection error? We present a stochastic optimization algorithm to optimize our probe signal. The main analysis framework used in this paper is that of revealed preferences from microeconomics.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 2 of its 41 references that are in the map. First posted on arXiv 1 Dec 2019.

Builds on

2 of 41 references.

References · 2

  1. 2016Partially Observed Markov Decision Processes. Problem Sets and Internet Supplementcited by 34
  2. 2019How to Calibrate your Adversary's Capabilities? Inverse Filtering for Counter-Autonomous Systemscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 0; 2022: 5; 2023: 2; 2024: 2; 2025: 2; 2026: 1.

13 papers.

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  1. 2022Inverse Extended Kalman Filter -- Part I: Fundamentalscited by 6
  2. 2022Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radarscited by 6
  3. 2022Counter-Adversarial Learning with Inverse Unscented Kalman Filtercited by 3
  4. 2022Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approachcited by 3
  5. 2020Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problemscited by 2
  6. 2023Inverse Cubature and Quadrature Kalman filterscited by 2
  7. 2025Uncovering Utility Functions from Observed Outcomescited by 2
  8. 2022How can a Radar Mask its Cognition?cited by 1
  9. 2023Inverse Unscented Kalman Filtercited by 0
  10. 2024Inverse Particle Filtercited by 0
  11. 2024Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensingcited by 0
  12. 2025Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approachcited by 0
1 more
  1. 2026Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
  2. 2022Adaptive ECCM for Mitigating Smart Jammers
  3. 2017Inverse Reinforcement Learning from Summary Data
  4. 2018Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior
  5. 2022Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner
  6. 2025Estimating cognitive biases with attention-aware inverse planning
  7. 2024Learning Utilities from Demonstrations in Markov Decision Processes
  8. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress