Compressive sensing of block-sparse signals recovery based on sparsity adaptive regularized orthogonal matching pursuit algorithm

2012 ◽  
Author(s):  
Qiang Zhao ◽  
Jinkuan Wang ◽  
Yinghua Han ◽  
Peng Han
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Sparse Signals ◽  
Matching Pursuit Algorithm

Least Support Orthogonal Matching Pursuit Algorithm With Prior Information

2014 ◽  
Vol 6 (2) ◽  
pp. 111-134 ◽  
Author(s):  
Israa Sh. Tawfic ◽  
Sema Koc Kayhan
Keyword(s):  
Numerical Experiments ◽  
Prior Information ◽  
Matching Pursuit ◽  
Recovery Process ◽  
Orthogonal Matching Pursuit ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Reconstruction Performance ◽  
Matching Pursuit Algorithm

Abstract This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.

scholarly journals A New Analysis for Support Performance with Block Generalized Orthogonal Matching Pursuit

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
C. Y. Xia ◽  
Z. L. Zhou ◽  
Chun-Bo Guo ◽  
Y. S. Hao ◽  
C. B. Hou
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Restricted Isometry Property ◽  
Orthogonal Matching Pursuit ◽  
Sparse Signals ◽  
Support Conditions ◽  
Generalized Orthogonal ◽  
Recovery Block ◽  
Block Structures

For recovering block-sparse signals with unknown block structures using compressive sensing, a block orthogonal matching pursuit- (BOMP-) like block generalized orthogonal matching pursuit (BgOMP) algorithm has been proposed recently. This paper focuses on support conditions of recovery of any K -sparse block signals incorporating BgOMP under the framework of restricted isometry property (RIP). The proposed support conditions guarantee that BgOMP can achieve accurate recovery block-sparse signals within k iterations.

scholarly journals High‐throughput and low‐area implementation of orthogonal matching pursuit algorithm for compressive sensing reconstruction

ETRI Journal ◽  
2019 ◽  
Vol 42 (3) ◽  
pp. 376-387
Author(s):  
Vu Quan Nguyen ◽  
Woo Hyun Son ◽  
Marek Parfieniuk ◽  
Luong Tran Nhat Trung ◽  
Sang Yoon Park
Keyword(s):  
Compressive Sensing ◽  
High Throughput ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Low Area ◽  
Matching Pursuit Algorithm

Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm

2018 ◽  
Vol 67 (9) ◽  
pp. 2058-2068 ◽  
Author(s):  
Carlos Morales-Perez ◽  
Jose Rangel-Magdaleno ◽  
Hayde Peregrina-Barreto ◽  
Juan Pablo Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Induction Motors ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Matching Pursuit Algorithm
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