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Cognitive sensing refers to a reconfigurable sensor that dynamically adaptsits sensing mechanism by using stochastic control to optimize its sensingresources. For example, cognitive radars are sophisticated dynamical systems;they use stochastic control to sense the environment, learn from it relevantinformation about the target and background, then adapt the radar sensor tosatisfy the needs of their mission. The last two decades have witnessed intenseresearch in cognitive/adaptive radars.This paper discusses addresses the nextlogical step, namely inverse cognitive sensing. By observing the emissions of asensor (e.g. radar or in general a controlled stochastic dynamical system) inreal time, how can we detect if the sensor is cognitive (rational utilitymaximizer) and how can we predict its future actions? The scientific challengesinvolve extending Bayesian filtering, inverse reinforcement learning andstochastic optimization of dynamical systems to a data-driven adversarialsetting. Our methodology transcends classical statistical signal processing(sensing and estimation/detection theory) to address the deeper issue of how toinfer strategy from sensing. The generative models, adversarial inferencealgorithms and associated mathematical analysis will lead to advances inunderstanding how sophisticated adaptive sensors such as cognitive radarsoperate.

Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar