2021/06/21 by Vikram Krishnamurthy, Kunal Pattanayak, Sandeep Gogineni +2 · 1 citation
Mathematics · Physics and Astronomy · Economics, Econometrics and Finance · #Statistical Distribution Estimation and Applications #Statistical Mechanics and Entropy #Financial Risk and Volatility Modeling
paper · doi:10.1109/taes.2021.3090901
This article considers three interrelated adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estimate of us based on the radar's actions and calibrate the radar's sensing accuracy. Second, using revealed preference from microeconomics, we formulate a nonparametric test to identify if the cognitive radar is a constrained utility maximizer with signal processing constraints. We consider two radar functionalities, namely, beam allocation and waveform design, with respect to which the cognitive radar is assumed to maximize its utility and construct a set-valued estimator for the radar's utility function. Finally, we discuss how to engineer interference at the physical layer level to confuse the radar that forces it to change its transmit waveform. The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level, and revealed preferences for identifying utility maximization at the systems level.