基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知

TN911.73; 在1-Bit压缩感知(compressive sensing,CS)框架下,将信号的稀疏结构先验引入广义稀疏Bayesian 学习(generalized sparse Bayesian learning,Gr-SBL),研究基于Gr-SBL的1-Bit CS重构.将广义线性模型与模式耦合稀疏Bayesian学习相结合,提出了一种基于广义模式耦合稀疏Bayesian学习1-Bit CS重构算法,简称为1-BitGr-PC-SBL算法.该算法将1-Bit CS重构问题迭代地分解成一系列标准CS重构问题,在信号稀疏模式未知的情况下,基于模式耦合稀疏Bayesian学习实现信号重...

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Published in系统工程与电子技术 Vol. 42; no. 12; pp. 2700 - 2707
Main Authors 司菁菁, 韩亚男, 张磊, 程银波
Format Journal Article
LanguageChinese
Published 燕山大学信息科学与工程学院,河北秦皇岛066004 01.12.2020
河北省信息传输与信号处理重点实验室,河北秦皇岛066004%燕山大学信息科学与工程学院,河北秦皇岛066004%河北农业大学海洋学院,河北秦皇岛066003
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ISSN1001-506X
DOI10.3969/j.issn.1001-506X.2020.12.05

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Abstract TN911.73; 在1-Bit压缩感知(compressive sensing,CS)框架下,将信号的稀疏结构先验引入广义稀疏Bayesian 学习(generalized sparse Bayesian learning,Gr-SBL),研究基于Gr-SBL的1-Bit CS重构.将广义线性模型与模式耦合稀疏Bayesian学习相结合,提出了一种基于广义模式耦合稀疏Bayesian学习1-Bit CS重构算法,简称为1-BitGr-PC-SBL算法.该算法将1-Bit CS重构问题迭代地分解成一系列标准CS重构问题,在信号稀疏模式未知的情况下,基于模式耦合稀疏Bayesian学习实现信号重构.进而,引入阈值自适应的二进制量化,设计了自适应阈值的1-Bit Gr-PC-SBL算法,进一步提升了算法的信号重构性能.
AbstractList TN911.73; 在1-Bit压缩感知(compressive sensing,CS)框架下,将信号的稀疏结构先验引入广义稀疏Bayesian 学习(generalized sparse Bayesian learning,Gr-SBL),研究基于Gr-SBL的1-Bit CS重构.将广义线性模型与模式耦合稀疏Bayesian学习相结合,提出了一种基于广义模式耦合稀疏Bayesian学习1-Bit CS重构算法,简称为1-BitGr-PC-SBL算法.该算法将1-Bit CS重构问题迭代地分解成一系列标准CS重构问题,在信号稀疏模式未知的情况下,基于模式耦合稀疏Bayesian学习实现信号重构.进而,引入阈值自适应的二进制量化,设计了自适应阈值的1-Bit Gr-PC-SBL算法,进一步提升了算法的信号重构性能.
Author 张磊
韩亚男
司菁菁
程银波
AuthorAffiliation 燕山大学信息科学与工程学院,河北秦皇岛066004;河北省信息传输与信号处理重点实验室,河北秦皇岛066004%燕山大学信息科学与工程学院,河北秦皇岛066004%河北农业大学海洋学院,河北秦皇岛066003
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Author_FL HAN Yanan
CHENG Yinbo
SI Jingjing
ZHANG Lei
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Keywords 1-Bit压缩感知
模式耦合
自适应阈值
广义稀疏Bayesian学习
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Publisher 燕山大学信息科学与工程学院,河北秦皇岛066004
河北省信息传输与信号处理重点实验室,河北秦皇岛066004%燕山大学信息科学与工程学院,河北秦皇岛066004%河北农业大学海洋学院,河北秦皇岛066003
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