DocumentCode :
3158525
Title :
Recovery of block sparse signals using the framework of block sparse Bayesian learning
Author :
Zhang, Zhilin ; Rao, Bhaskar D.
Author_Institution :
ECE Dept., Univ. of California at San Diego, La Jolla, CA, USA
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
3345
Lastpage :
3348
Abstract :
In this paper we study the recovery of block sparse signals and extend conventional approaches in two important directions; one is learning and exploiting intra-block correlation, and the other is generalizing signals´ block structure such that the block partition is not needed to be known for recovery. We propose two algorithms based on the framework of block sparse Bayesian learning (bSBL). One algorithm, directly derived from the framework, requires a priori knowledge of the block partition. Another algorithm, derived from an expanded bSBL framework using the generalization method, can be used when the block partition is unknown. Experiments show that they have superior performance to state-of-the-art algorithms.
Keywords :
signal processing; block sparse Bayesian learning; block sparse signals recovery; expanded bSBL framework; generalization method; intra-block correlation; Bayesian methods; Bismuth; Clustering algorithms; Compressed sensing; Correlation; Covariance matrix; Partitioning algorithms; Block Sparse Model; Cluster Structure; Compressed Sensing; Sparse Bayesian Learning; Sparse Signal Recovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2012.6288632
Filename :
6288632
Link To Document :
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