Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model

LI Shaodong; CHEN Wenfeng; YANG Jun; MA Xiaoyan

doi:10.11999/JEIT151131

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1731-1737

LI Shaodong, CHEN Wenfeng, YANG Jun, MA Xiaoyan. Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131

Citation:

LI Shaodong, CHEN Wenfeng, YANG Jun, MA Xiaoyan. Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131

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Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model

doi: 10.11999/JEIT151131

Received Date: 2015-10-10
Rev Recd Date: 2016-02-25
Publish Date: 2016-07-19

Abstract

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.
- Multiple measurement vectors model,
- Fast Orthogonal Matching Pursuit (FOMP) algorithm,
- Number of iterations,
- Bayesian test

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