An Optimization-Oriented Algorithm for Sparse Signal Reconstruction

Fulin Li; Shaohua Hong; Yujie Gu; Lin Wang

doi:10.1109/lsp.2019.2897458

An Optimization-Oriented Algorithm for Sparse Signal Reconstruction

Fulin Li, Shaohua Hong, Yujie Gu, Lin Wang

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.

Original language	English
Article number	8633941
Pages (from-to)	515-519
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	26
Issue number	3
DOIs	https://doi.org/10.1109/lsp.2019.2897458
State	Published - Mar 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

Manuscript received November 5, 2018; revised December 26, 2018; accepted January 30, 2019. Date of publication February 4, 2019; date of current version February 14, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61671395 and in part by the National Science Foundation of Guangdong Province of China under Grant 2018A030313710. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Qing Ling. (Corresponding author: Shaohua Hong.) F. Li, S. Hong, and L. Wang are with the Department of Communication Engineering, Xiamen University, Xiamen 361005, China (e-mail:,youthbeyond@ yahoo.com; [email protected]; [email protected]).

Funders	Funder number
National Natural Science Foundation of China	61671395
Natural Science Foundation of Guangdong Province	2018A030313710

Keywords

Compressive sensing
global optimization
greedy algorithms
optimization-oriented algorithm

Access to Document

10.1109/lsp.2019.2897458

Cite this

@article{17a5af8927054e098d608456c095f154,

title = "An Optimization-Oriented Algorithm for Sparse Signal Reconstruction",

abstract = "Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.",

keywords = "Compressive sensing, global optimization, greedy algorithms, optimization-oriented algorithm",

author = "Fulin Li and Shaohua Hong and Yujie Gu and Lin Wang",

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

year = "2019",

month = mar,

doi = "10.1109/lsp.2019.2897458",

language = "אנגלית",

volume = "26",

pages = "515--519",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3",

}

TY - JOUR

T1 - An Optimization-Oriented Algorithm for Sparse Signal Reconstruction

AU - Li, Fulin

AU - Hong, Shaohua

AU - Gu, Yujie

AU - Wang, Lin

PY - 2019/3

Y1 - 2019/3

N2 - Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.

AB - Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.

KW - Compressive sensing

KW - global optimization

KW - greedy algorithms

KW - optimization-oriented algorithm

UR - http://www.scopus.com/inward/record.url?scp=85062216788&partnerID=8YFLogxK

U2 - 10.1109/lsp.2019.2897458

DO - 10.1109/lsp.2019.2897458

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:85062216788

SN - 1070-9908

VL - 26

SP - 515

EP - 519

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 3

M1 - 8633941

ER -

An Optimization-Oriented Algorithm for Sparse Signal Reconstruction

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this