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    Support agnostic Bayesian matching pursuit for block sparse signals

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    Type
    Conference Paper
    Authors
    Masood, Mudassir cc
    Al-Naffouri, Tareq Y. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2013-05
    Permanent link to this record
    http://hdl.handle.net/10754/564708
    
    Metadata
    Show full item record
    Abstract
    A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.
    Citation
    Masood, M., & Al-Naffouri, T. Y. (2013). Support agnostic Bayesian matching pursuit for block sparse signals. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. doi:10.1109/icassp.2013.6638540
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 IEEE International Conference on Acoustics, Speech and Signal Processing
    Conference/Event name
    2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
    ISBN
    9781479903566
    DOI
    10.1109/ICASSP.2013.6638540
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICASSP.2013.6638540
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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