Identifying Cognitive Radars - Inverse Reinforcement Learning using Revealed Preferences

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...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 68; S. 1
Hauptverfasser:	Krishnamurthy, Vikram, Angley, Daniel, Evans, Rob, Moran, Bill
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	adversarial signal processing Afriat's theorem algebraic Riccati equation Algorithms Bayes methods beam scheduling Cognitive radar Constraints Covariance matrix detection economics-based-rationality Eigenvalues Error detection identifying cognitive behavior inverse reinforcement learning Kalman filter tracker Learning (artificial intelligence) Machine learning Maximization Noise Optimization Probes Radar Radar detection Radar tracking revealed preferences Riccati equation spectral revealed preferences Statistical tests stochastic gradient algorithm waveform selection Waveforms
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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