Block sparse signal recovery with a Block-Toeplitz structured measurement matrix

Recently, many applications about the recovery of block sparse signals have arisen, which can be casted as the recovery of a block sparse signal x from a measurement equation y = Φx. In this paper, we investigate block sparse signal recovery problems when Φ is assumed to be a block-concatenation of...

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Bibliographic Details
Main Authors: Huang, Boxue, Zhou, Tong
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Algorithms
block sparse
compressive sensing
Computer simulation
Conferences
Gaussian distribution
Greedy algorithms
Matching pursuit algorithms
Mathematical analysis
Minimization
Parameter estimation
Recovery
Signal processing algorithms
Sparse matrices
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Summary:Recently, many applications about the recovery of block sparse signals have arisen, which can be casted as the recovery of a block sparse signal x from a measurement equation y = Φx. In this paper, we investigate block sparse signal recovery problems when Φ is assumed to be a block-concatenation of Toeplitz matrices. The algorithm of StOMP is extended to the block sparse case, and the algorithm of tBlock-StOMP is proposed. Specifically, tBlock-StOMP combines advantages of StOMP with the structural characteristics of Φ to pursue high efficiency in block sparse signal recovery. Furthermore, a modified algorithm of tBlock-StOMP, termed mtBlock-StOMP, is proposed. Compared with many other recovery algorithms, numerical simulations demonstrate that tBlock-StOMP as well as mtBlock-StOMP results in evident effectiveness in block sparse reconstruction problems.
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2016.7554108