Open AccessWeighted double‐backtracking matching pursuit for block‐sparse reconstruction
Author(s) -
Pei Liye,
Jiang Hua,
Li Ming
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0036
Subject(s) - backtracking , matching pursuit , block (permutation group theory) , computer science , algorithm , convergence (economics) , matching (statistics) , sparse approximation , set (abstract data type) , pattern recognition (psychology) , artificial intelligence , mathematics , mathematical optimization , compressed sensing , geometry , economics , programming language , economic growth , statistics
This study presents a new method for the reconstruction of block‐sparse signals with and without noisy perturbations, termed weighted double‐backtracking matching pursuit (WDBMP). Unlike anterior block‐sparse reconstruction algorithms, WDBMP requires no prior knowledge about block length and boundaries. It not only refines the current approximation based on energy, but also takes advantage of block structure to refine the chosen support set, and thus to improve the recovery performance. Moreover, the authors propose weighted proxy to select the candidates, which can increase the probability of selecting correct supports and improve the convergence speed. Experimental results show that the proposed algorithm owns better recovery quality and requires fewer iterations to converge compared with the existing block‐sparse reconstruction algorithms without knowing the block‐sparse boundaries.