Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

Jinming Wen; Jian Weng; Chao Tong; Chao Ren; Zhengchun Zhou

doi:10.1109/TVT.2019.2919612

Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

Jinming Wen
, Jian Weng^*
, Chao Tong
, Chao Ren
, Zhengchun Zhou

^*此作品的通讯作者

计算机学院

Jinan University
University of Science and Technology Beijing
Southwest Jiaotong University

科研成果: 期刊稿件 › 文章 › 同行评审

60 引用（Scopus）

摘要

The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||₁ - ||x||₂ is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

源语言	英语
文章编号	8723544
页（从-至）	6847-6854
页数	8
期刊	IEEE Transactions on Vehicular Technology
卷	68
期	7
DOI	https://doi.org/10.1109/TVT.2019.2919612
出版状态	已出版 - 7月 2019

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title = "Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm",

abstract = "The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.",

keywords = "Sparse signal recovery, mutual coherence, sufficient condition",

author = "Jinming Wen and Jian Weng and Chao Tong and Chao Ren and Zhengchun Zhou",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = jul,

doi = "10.1109/TVT.2019.2919612",

language = "英语",

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T1 - Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

AU - Wen, Jinming

AU - Weng, Jian

AU - Tong, Chao

AU - Ren, Chao

AU - Zhou, Zhengchun

PY - 2019/7

Y1 - 2019/7

N2 - The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

AB - The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

KW - Sparse signal recovery

KW - mutual coherence

KW - sufficient condition

UR - https://www.scopus.com/pages/publications/85069482267

U2 - 10.1109/TVT.2019.2919612

DO - 10.1109/TVT.2019.2919612

M3 - 文章

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VL - 68

SP - 6847

EP - 6854

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

IS - 7

M1 - 8723544

ER -

Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

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