多量测向量模型下基于贝叶斯检验的快速OMP算法研究

doi:10.11999/JEIT151131

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Abstract

There are two issues in the Sparse Reconstruction (SR) algorithm of Multiple Measurement Vectors (MMV). One is the high computation complexity and the other is that redundant support set can not be effectively removed. In order to improve the efficiency and accuracy of SR algorithm simultaneously for MMV model, a Fast Orthogonal Matching Pursuit algorithm based on Bayesian Test (FOMP-BT) is presented in this paper. Firstly, the total number of iterations and the computation of each iteration are reduced through the new atomic group selection and warm start matrix inversion, thus the efficiency of the algorithm is improved. Secondly, using the idea of the Bayesian test to eliminate redundant support set, the accuracy of reconstruction is improved. Finally, the theoretical analysis of the algorithm is carried out from the aspects of parameter selection and computation complexity. The simulation results show that the proposed algorithm has the advantages of high accuracy, fast speed and good robustness to noise.

Key words： Multiple measurement vectors model Fast Orthogonal Matching Pursuit (FOMP) algorithm Number of iterations Bayesian test

Received: 10 October 2015 Published: 07 April 2016

PACS:

TN957.52

Corresponding Authors: LI Shaodong E-mail: liying198798@126.com

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	LI Shaodong
	CHEN Wenfeng
	YANG Jun
	MA Xiaoyan

Cite this article:

LI Shaodong,CHEN Wenfeng,YANG Jun等. Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model[J]. JEIT, 2016, 38(7): 1731-1737.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT151131 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I7/1731

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