10 citations · 11 across the 10 of their papers we have counts for
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Approximate MLE of High-Dimensional STAP Covariance Matrices with Banded & Spiked Structure -- A Convex Relaxation Approach
Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy +3
Estimating the clutter-plus-noise covariance matrix in high-dimensional STAP is challenging in the presence of Internal Clutter Motion (ICM) and a high noise floor. The problem bec…
A Digital Engineering Approach to Testing Modern AI and Complex Systems
Joseph R. Guerci, Sandeep Gogineni, Robert W. Schutz +5
Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and va…
Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang +1
In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algo…
Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach
Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy +2
In this paper, we exploit the spiked covariance structure of the clutter plus noise covariance matrix for radar signal processing. Using state-of-the-art techniques high dimensiona…
High Fidelity RF Clutter Modeling and Simulation
Sandeep Gogineni, Joseph R. Guerci, Hoan K. Nguyen +4
In this paper, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation (M&S) techniques. Traditional statistical approximation based…
Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference
Vikram Krishnamurthy, Kunal Pattanayak, Sandeep Gogineni +2
This paper considers three inter-related adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estim…