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Statistical Recovery of Simultaneously Sparse Time-Varying Signals From Multiple Measurement Vectors

Title
Statistical Recovery of Simultaneously Sparse Time-Varying Signals From Multiple Measurement Vectors
Author
최준원
Keywords
Compressed sensing; simultaneously sparse signal; multiple measurement vector; expectation-maximization (EM) algorithm; maximum likelihood estimation
Issue Date
2015-10
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON SIGNAL PROCESSING, v. 63, NO 22, Page. 6136-6148
Abstract
In this paper, we propose a new sparse signal recovery algorithm, referred to as sparse Kalman tree search (sKTS), that provides a robust reconstruction of the sparse vector when the sequence of correlated observation vectors are available. The proposed sKTS algorithm builds on expectation-maximization (EM) algorithm and consists of two main operations: 1) Kalman smoothing to obtain the a posteriori statistics of the source signal vectors and 2) greedy tree search to estimate the support of the signal vectors. Through numerical experiments, we demonstrate that the proposed sKTS algorithm is effective in recovering the sparse signals and performs close to the Oracle (genie-based) Kalman estimator.
URI
http://ieeexplore.ieee.org/document/7174568/http://hdl.handle.net/20.500.11754/28197
ISSN
1053-587X; 1941-0476
DOI
10.1109/TSP.2015.2463259
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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