10 citations · 11 across the 10 of their papers we have counts for
10 papers
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…
Fisher Information Approach for Masking the Sensing Plan: Applications in Multifunction Radars
Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy +2
How to design a Markov Decision Process (MDP) based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary? The radar controller pur…
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…
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang +3
Recent works exploring data-driven approaches to classical problems in adaptive radar have demonstrated promising results pertaining to the task of radar target localization. Via t…
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…
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang +3
Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper p…
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…
Toward Data-Driven STAP Radar
Shyam Venkatasubramanian, Chayut Wongkamthong, Mohammadreza Soltani +5
Using an amalgamation of techniques from classical radar, computer vision, and deep learning, we characterize our ongoing data-driven approach to space-time adaptive processing (ST…
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…