Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars

Pattanayak, Kunal; Krishnamurthy, Vikram; Berry, C. P. L.

doi:10.48550/arxiv.2205.01794

Cited by 1 publication

(4 citation statements)

References 26 publications

(57 reference statements)

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“…This paper builds significantly on our previous work [4] on ECM for identifying cognitive radars, and [53], [54], [55] on ECCM for masking radar cognition. Theorem 6 extends IRL for cognitive radars [4] when the radar faces multiple resource constraints.…”

Section: Conclusion and Extensionsmentioning

confidence: 87%

“…Theorem 7 generalizes the cognition masking result of [53] to multiple constraints. Our previous works [53], [54], [55] assume optimal adversarial IRL via Afriat's theorem. This paper generalizes cognition masking to sub-optimal adversarial IRL algorithms.…”

Section: Conclusion and Extensionsmentioning

confidence: 99%

“…The exponents for the Cobb-Douglas utility function (54), referred to as elasticity parameters in consumer economics literature, parameterize the marginal utility per consumer good.…”

Section: Example Optimal Beam Allocationmentioning

confidence: 99%

“…Also, observe that both the utility function and cost are monotone in the transmission intensities -higher beam intensity yields a larger utility but is also more costly. We assume that: (1) each target i is equipped with a radar detector and can estimate the beam intensity β k (i) the enemy's radar directs towards it -this assumption facilitates the targets to carry out adversarial IRL attacks on the radar, and (2) the adversarial targets know the radar is maximizing the Cobb-Douglas utility function (54), but does not know the radar's budget constraint g(β) ≤ γ k and is the adversary's IRL objective in the beam allocation context as discussed below. IRL for optimal beam allocation.…”

Section: Example Optimal Beam Allocationmentioning

confidence: 99%

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How Can a Cognitive Radar Mask its Cognition?

Pattanayak

Krishnamurthy

Berry

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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 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 countercountermeasures (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.

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Section: Conclusion and Extensionsmentioning

confidence: 87%

Section: Conclusion and Extensionsmentioning

confidence: 99%

“…The exponents for the Cobb-Douglas utility function (54), referred to as elasticity parameters in consumer economics literature, parameterize the marginal utility per consumer good.…”

Section: Example Optimal Beam Allocationmentioning

confidence: 99%

Section: Example Optimal Beam Allocationmentioning

confidence: 99%

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