ECE Seminar* Sparse Sampling to Maximize Detection of a Known Signal in Nonwhite Gaussian Noise
When: Friday,
April 21, 2023
10:30 AM
-
12:30 PM
Where: > See description for location
Cost: Free
Description: Topic: Sparse Sampling to Maximize Detection of a Known Signal in Nonwhite Gaussian Noise
Speaker: Dr. Kaushallya Adhikari, Assistant Professor, Electrical, Computer and Biomedical Engineering, University of Rhode Island
Location: Science & Engineering Building (SENG), Room 222
Abstract:
We address the problem of sparse sampling pattern design to maximize detection for a deterministic signal in colored noise. We model a colored noise as a continuous-time autoregressive process, which is obtained by passing a white noise through a causal linear-time invariant filter. This noise modeling is crucial to the development of the optimal sampling pattern design for a given number of sensors. We obtain a closed form expression for the whitening filter and consequently, for the Kullback-Leibler divergence at the whitening filter output, which is the detection index. The optimum sampling pattern is obtained by evaluating the detection index at Nyquist sampling rate, rank ordering the samples, and selecting the maximum values. We present some examples to illustrate the proposed procedure. We also extend our method to two-dimensional sampling. The advantage of our approach is its low computational complexity for both one-dimensional and two-dimensional cases and that optimality is guaranteed.
Biography:
Kaushallya Adhikari is an Assistant Professor of Electrical Engineering at the University of Rhode Island. She earned M.S. and Ph.D. degrees in Electrical Engineering from University of Massachusetts Dartmouth in 2011 and 2016, respectively. She received the EURASIP Best Paper Award in 2018 for a journal article in Signal Processing. She teaches courses in Signal Processing. Her research focuses on Array Signal Processing. Her current research has been sponsored by the Office of Naval Research Young Investigator Program. This research is on using Information Theory to design sparse sampling patterns, maximizing the detection of signals.
The Seminar is open to the public free of charge.
*For further information, please contact Dr. John R. Buck via email at jbuck@umassd.edu.
Speaker: Dr. Kaushallya Adhikari, Assistant Professor, Electrical, Computer and Biomedical Engineering, University of Rhode Island
Location: Science & Engineering Building (SENG), Room 222
Abstract:
We address the problem of sparse sampling pattern design to maximize detection for a deterministic signal in colored noise. We model a colored noise as a continuous-time autoregressive process, which is obtained by passing a white noise through a causal linear-time invariant filter. This noise modeling is crucial to the development of the optimal sampling pattern design for a given number of sensors. We obtain a closed form expression for the whitening filter and consequently, for the Kullback-Leibler divergence at the whitening filter output, which is the detection index. The optimum sampling pattern is obtained by evaluating the detection index at Nyquist sampling rate, rank ordering the samples, and selecting the maximum values. We present some examples to illustrate the proposed procedure. We also extend our method to two-dimensional sampling. The advantage of our approach is its low computational complexity for both one-dimensional and two-dimensional cases and that optimality is guaranteed.
Biography:
Kaushallya Adhikari is an Assistant Professor of Electrical Engineering at the University of Rhode Island. She earned M.S. and Ph.D. degrees in Electrical Engineering from University of Massachusetts Dartmouth in 2011 and 2016, respectively. She received the EURASIP Best Paper Award in 2018 for a journal article in Signal Processing. She teaches courses in Signal Processing. Her research focuses on Array Signal Processing. Her current research has been sponsored by the Office of Naval Research Young Investigator Program. This research is on using Information Theory to design sparse sampling patterns, maximizing the detection of signals.
The Seminar is open to the public free of charge.
*For further information, please contact Dr. John R. Buck via email at jbuck@umassd.edu.
Contact: > See Description for contact information
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering