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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

李少東, 陳文峰, 楊軍, 馬曉巖. 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[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131
引用本文: 李少東, 陳文峰, 楊軍, 馬曉巖. 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[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131
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

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

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

  • 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)。
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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

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