Structure-Based Bayesian Sparse Reconstruction

Citation data:

IEEE Transactions on Signal Processing, ISSN: 1053-587X, Vol: 60, Issue: 12, Page: 6354-6367

Publication Year:
2012
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Citations 14
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Repository URL:
http://hdl.handle.net/10754/562446
DOI:
10.1109/tsp.2012.2215029
Author(s):
Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE); Institute of Electrical and Electronics Engineers
Tags:
Computer Science; Engineering; Bayesian methods; compressed sensing; compressive sampling; signal recovery; sparse signal reconstruction
article description
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.