Open AccessGamma‐distribution‐based logit weighted block orthogonal matching pursuit for compressed sensing
Author(s) -
Lu Liyang,
Xu Wenbo,
Cui Yupeng,
Dang Yifei,
Wang Siye
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.1676
Subject(s) - matching pursuit , compressed sensing , block (permutation group theory) , matching (statistics) , selection (genetic algorithm) , algorithm , computer science , mathematical optimization , pattern recognition (psychology) , mathematics , artificial intelligence , statistics , geometry
Block orthogonal matching pursuit is an efficient reconstruction algorithm in compressed sensing, which exploits block sparsity during support index selection. In this letter, to further improve the performance, the authors propose two block sparse reconstruction algorithms by incorporating the prior information of block support probability. Based on Gamma distribution approximation, such information is formulated as an additive term during index selection. Moreover, the second algorithm extends the first one to the scenario with inaccurate prior information by introducing an additional judging mechanism with block correlation and prior factor simultaneously. Numerical results show that the proposed algorithms outperform existing algorithms.