IEICE TRANSACTIONS on Fundamentals
Improved Analysis for SOMP Algorithm in Terms of Restricted Isometry Property
Summary :
In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}< rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.2 pp.533-537
- Publication Date
- 2020/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAL2055
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Xiaobo ZHANG
Zhengzhou University of Light Industry
Wenbo XU
Beijing University of Posts and Telecommunications
Yan TIAN
Beijing University of Posts and Telecommunications
Jiaru LIN
Beijing University of Posts and Telecommunications
Wenjun XU
Beijing University of Posts and Telecommunications
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Xiaobo ZHANG, Wenbo XU, Yan TIAN, Jiaru LIN, Wenjun XU, "Improved Analysis for SOMP Algorithm in Terms of Restricted Isometry Property" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 2, pp. 533-537, February 2020, doi: 10.1587/transfun.2019EAL2055.
Abstract: In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}< rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAL2055/_p
Copy
@ARTICLE{e103-a_2_533,
author={Xiaobo ZHANG, Wenbo XU, Yan TIAN, Jiaru LIN, Wenjun XU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improved Analysis for SOMP Algorithm in Terms of Restricted Isometry Property},
year={2020},
volume={E103-A},
number={2},
pages={533-537},
abstract={In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}< rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.},
keywords={},
doi={10.1587/transfun.2019EAL2055},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Improved Analysis for SOMP Algorithm in Terms of Restricted Isometry Property
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 533
EP - 537
AU - Xiaobo ZHANG
AU - Wenbo XU
AU - Yan TIAN
AU - Jiaru LIN
AU - Wenjun XU
PY - 2020
DO - 10.1587/transfun.2019EAL2055
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2020
AB - In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}< rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.
ER -
English
