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    Structure-based bayesian sparse reconstruction

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    Type
    Article
    Authors
    Quadeer, Ahmed Abdul
    Al-Naffouri, Tareq Y. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2012-12
    Preprint Posting Date
    2012-07-16
    Permanent link to this record
    http://hdl.handle.net/10754/562446
    
    Metadata
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    Abstract
    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.
    Sponsors
    Manuscript received April 24, 2012; revised July 23, 2012; accepted July 30, 2012. Date of publication August 23, 2012; date of current version November 20, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Z. Jane Wang. This work was partially supported by SABIC through an internally funded project from DSR, KFUPM (Project No. SB101006) and partially by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at KFUPM (Project No. 09-ELE763-04) as part of the National Science, Technology and Innovation Plan. The work of T. Y. Al-Naffouri was also supported by the Fullbright Scholar Program.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2012.2215029
    arXiv
    arXiv:1207.3847
    Additional Links
    http://arxiv.org/abs/arXiv:1207.3847v1
    ae974a485f413a2113503eed53cd6c53
    10.1109/TSP.2012.2215029
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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