scholarly journals Performance Analysis of Joint-Sparse Recovery from Multiple Measurement Vectors via Convex Optimization: Which Prior Information is Better?

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3739-3754 ◽  
Author(s):  
Shih-Wei Hu ◽  
Gang-Xuan Lin ◽  
Sung-Hsien Hsieh ◽  
Chun-Shien Lu
Keyword(s):  
Performance Analysis ◽  
Convex Optimization ◽  
Prior Information ◽  
Sparse Recovery ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

scholarly journals Sparse Recovery With Block Multiple Measurement Vectors Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9470-9475
Author(s):  
Yanli Shi ◽  
Libo Wang ◽  
Rong Luo
Keyword(s):  
Sparse Recovery ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

scholarly journals A Hybrid Orthogonal Forward-Backward Pursuit Algorithm for Partial Fourier Multiple Measurement Vectors Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haiqiang Liu ◽  
Gang Hua ◽  
Aichun Zhu ◽  
Hongsheng Yin ◽  
Yonggang Xu
Keyword(s):  
Prior Knowledge ◽  
Prior Information ◽  
Matching Pursuit ◽  
Computing Time ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Second Stage ◽  
Support Set ◽  
Simulation Results ◽  
Two Stages

In solving the partial Fourier Multiple Measurement Vectors (FMMV) problem, existing greedy pursuit algorithms such as Simultaneous Orthogonal Matching Pursuit (SOMP), Simultaneous Subspace Pursuit (SSP), Hybrid Matching Pursuit (HMP), and Forward-Backward Pursuit (FBP) suffer from low recovery ability or need sparsity as a prior information. This paper combines SOMP and FBP to propose a Hybrid Orthogonal Forward-Backward Pursuit (HOFBP) algorithm. As an iterative algorithm, each iteration of HOFBP consists of two stages. In the first stage, α indices selected by SOMP are added to the support set. In the second stage, the support set is shrank by removing β indices. The choice of α and β is critical to the performance of this algorithm. The simulation results showed that, by using proper parameters, HOFBP has better performance than other greedy pursuit algorithms at the expense of more computing time in some cases. HOFBP does not need sparsity as a prior knowledge.

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