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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 68; p. 1
Main Authors: Krishnamurthy, Vikram, Angley, Daniel, Evans, Rob, Moran, Bill
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.3013516