多候选集广义正交匹配追踪算法

doi:10.3969/j.issn.0255-8297.2017.02.010

Abstract

Abstract:

A multi-candidate set of generalized orthogonal matching pursuit algorithm is proposed to improve precision of greedy algorithms for compressed sensing. Multiple atoms are chosen as multiple candidates based on correlation of the inner product of observation matrix and residual. In the iteration, the multiple atoms are added to the multiple candidate sets, resulting in fast convergence of the algorithm. The candidate set with the smallest residuals is chosen as the final support set so that the sparse signal is exactly rebuild. Compared with other algorithms, experimental results show that the proposed algorithm has better recovery performance and lower recovery complexity.

Key words: multi-candidate set, matching pursuit, compressed sensing, reconstruction algorithm

CLC Number:

TN911.7

TIAN Jin-peng, LIU Xiao-juan, LIU Yan-ping, XUE Ying, ZHENG Guo-xin. Multi-candidate Set of Generalized Orthogonal Matching Pursuit Algorithm[J]. Journal of Applied Sciences, 2017, 35(2): 233-243.

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Multi-candidate Set of Generalized Orthogonal Matching Pursuit Algorithm

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