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 | |
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