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Iterative Reweighed Least Squares Algorithm for Block-Sparse Compressed Sensing
Author(s): WANG Wen-dong, WANG Yao, WANG Jian-jun
Pages: 922-
928
Year: 2015
Issue:
5
Journal: Acta Electronica Sinica
Keyword: compressed sensing; iterative reweighed least squares algorithm; block-sparse signals; error estimation; local con-vergence;
Abstract: Compressed sensing is a novel theory for signal processing which breaks through the sampling limitation based on traditional Shannon sampling theory ,and makes it into reality that one can efficiently acquire and exactly reconstruct a signal using the prior knowledge that it is sparse or compressible .In reality ,however ,some signals exhibit additional structures ,the typical exam-ple is the signal which is called block-sparse signal ,i .e .,the non-zero coefficients appear in a few fixed blocks .In order to tackle such block-sparse signal ,in this paper we investigate the iterative reweighed least squares algorithm for block-sparse compressed sensing .The error estimation and local convergence analysis have been established .We simultaneously demonstrate the effectiveness of the iterative reweighed least squares algorithm (IRLS ) for block-sparse compressed sensing by simulation results .
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