多量測(cè)向量模型下基于貝葉斯檢驗(yàn)的快速OMP算法研究

李少東; 陳文峰; 楊軍; 馬曉巖

doi:10.11999/JEIT151131

多量測(cè)向量模型下基于貝葉斯檢驗(yàn)的快速OMP算法研究

doi: 10.11999/JEIT151131

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出版歷程
- 收稿日期: 2015-10-10
- 修回日期: 2016-02-25
- 刊出日期: 2016-07-19

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

摘要

摘要: 目前多量測(cè)向量(Multiple Measurement Vectors, MMV)模型的稀疏重構(gòu)算法存在兩個(gè)問題：計(jì)算復(fù)雜度高和當(dāng)重構(gòu)的支撐集存在冗余時(shí)無法有效剔除。為同時(shí)提高M(jìn)MV模型的重構(gòu)效率和重構(gòu)精度，該文提出一種MMV模型下基于貝葉斯檢驗(yàn)的快速正交匹配追蹤(Fast Orthogonal Matching Pursuit based on Bayesian Testing, FOMP-BT)算法。首先，通過新原子組選和warm start求逆的思想來減少算法總的迭代次數(shù)以及每次迭代的運(yùn)算量，以提高算法的重構(gòu)效率；其次，利用貝葉斯檢驗(yàn)的思想剔除冗余支撐集以提高重構(gòu)精度；最后對(duì)所研究的算法從參數(shù)選擇以及計(jì)算復(fù)雜度等方面進(jìn)行了理論分析。仿真結(jié)果表明，所提算法具有重構(gòu)精度高、速度快以及對(duì)噪聲有較好的魯棒性等優(yōu)勢(shì)。
- 多量測(cè)向量模型 /
- 快速正交匹配追蹤算法 /
- 迭代次數(shù) /
- 貝葉斯檢驗(yàn)
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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