Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Gogineni, Sandeep"'
Autor:
Jain, Shashwat, Krishnamurthy, Vikram, Rangaswamy, Muralidhar, Kang, Bosung, Gogineni, Sandeep
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 purposefully minimizes the Fisher information of its emissions so that an
Externí odkaz:
http://arxiv.org/abs/2403.15966
In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algorithms to reach the theoretical limits of the Cramer Rao Bound (CRB) f
Externí odkaz:
http://arxiv.org/abs/2401.11176
Autor:
Venkatasubramanian, Shyam, Gogineni, Sandeep, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
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 the use of space-time adaptive processing (STAP) techniques and convolu
Externí odkaz:
http://arxiv.org/abs/2303.08241
Autor:
Jain, Shashwat, Krishnamurthy, Vikram, Rangaswamy, Muralidhar, Kang, Bosung, Gogineni, Sandeep
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 dimensional statistics, we propose a nonlinear shrinkage-based rotation invarian
Externí odkaz:
http://arxiv.org/abs/2302.02045
Autor:
Venkatasubramanian, Shyam, Gogineni, Sandeep, Kang, Bosung, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target loc
Externí odkaz:
http://arxiv.org/abs/2209.02890
Autor:
Gogineni, Sandeep, Guerci, Joseph R., Nguyen, Hoan K., Bergin, Jameson S., Kirk, David R., Watson, Brian C., Rangaswamy, Muralidhar
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 methods will be reviewed followed by more accurate physics-based stoc
Externí odkaz:
http://arxiv.org/abs/2202.05666
Autor:
Venkatasubramanian, Shyam, Wongkamthong, Chayut, Soltani, Mohammadreza, Kang, Bosung, Gogineni, Sandeep, Pezeshki, Ali, Rangaswamy, Muralidhar, Tarokh, Vahid
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 (STAP) radar. We generate a rich example dataset of received radar signal
Externí odkaz:
http://arxiv.org/abs/2201.10712
Autor:
Krishnamurthy, Vikram, Pattanayak, Kunal, Gogineni, Sandeep, Kang, Bosung, Rangaswamy, Muralidhar
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 estimate of us based on the radar's actions and calibrate the radar's sensi
Externí odkaz:
http://arxiv.org/abs/2008.01559
Akademický článek
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Publikováno v:
In Signal Processing August 2020 173
