10 citations · 13 across the 13 of their papers we have counts for
13 papers
Learning-Augmented Ski Rental with Discrete Distributions: A Bayesian Approach
Bosun Kang, Hyejun Park, Chenglin Fan
We revisit the classic ski rental problem through the lens of Bayesian decision-making and machine-learned predictions. While traditional algorithms minimize worst-case cost withou…
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…
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki +2
We present a large-scale dataset called RASPNet for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar…
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…
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…
Spatio-Spectral Radar Beampattern Design for Co-existence with Wireless Communication Systems
Bosung Kang, Omar Aldayel, Vishal Monga +1
We address the problem of designing a transmit beampattern for multiple-input multiple-output (MIMO) radar considering co-existence with wireless communication systems. The designe…
Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP)
Bosung Kang
Estimating the disturbance or clutter covariance is a centrally important problem in radar space time adaptive processing (STAP). The disturbance covariance matrix should be inferr…
Robust Covariance Estimation under Imperfect Constraints using an Expected Likelihood Approach
Bosung Kang, Vishal Monga, Muralidhar Rangaswamy +1
We address the problem of structured covariance matrix estimation for radar space-time adaptive processing (STAP). A priori knowledge of the interference environment has been explo…