Journal article

Identifying Cognitive Radars - Inverse Reinforcement Learning Using Revealed Preferences

V Krishnamurthy, D Angley, R Evans, B Moran

IEEE Transactions on Signal Processing | IEEE | Published : 2020

Abstract

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, ..

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Grants

Awarded by Air Force Office of Scientific Research


Awarded by US Army Research Office


Funding Acknowledgements

The associate editor coordinating the review of this manuscript and approving it for publicationwas Dr. Gang Li. Thisworkwas supported in part by the Air Force Office of Scientific Research under Grant FA9550-18-1-0007 and in part by US Army Research Office under Grant W911NF-19-1-0365, and in part by theDynamicData Driven Application Systems Program.