activity
20192022
most citedHow can a Radar Mask its Cognition?

2 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

eess.SP2022

Adaptive ECCM for Mitigating Smart Jammers

Kunal Pattanayak, Shashwat Jain, Vikram Krishnamurthy +1

This paper considers adaptive radar electronic counter-counter measures (ECCM) to mitigate ECM by an adversarial jammer. Our ECCM approach models the jammer-radar interaction as a…

eess.SP20222 cited

How can a Radar Mask its Cognition?

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

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 cogn…

cs.LG2022

Inverse-Inverse Reinforcement Learning. How to Hide Strategy from an Adversarial Inverse Reinforcement Learner

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

Inverse reinforcement learning (IRL) deals with estimating an agent's utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate…

eess.SP2022

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

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

This paper considers meta-cognitive radars in an adversarial setting. A cognitive radar optimally adapts its waveform (response) in response to maneuvers (probes) of a possibly adv…

eess.SP2021

How can a Cognitive Radar Mask its Cognition?

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

We study how a cognitive radar can mask (hide) its cognitive ability from an adversarial jamming device. Specifically, if the radar optimally adapts its waveform based on adversari…

cs.LG2021

Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification

Kunal Pattanayak, Vikram Krishnamurthy

Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs? We demonstrate that deep CNNs behave…