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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Veröffentlicht in:Chinese Control Conference S. 4865 - 4870
Hauptverfasser: Huang, Boxue, Zhou, Tong
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: TCCT 01.07.2016
Schlagworte:
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
ISSN:1934-1768
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Zusammenfassung: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.
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SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7554108