Orthogonal-Gradient Measurement Matrix Construction Algorithm

TIAN Shujuan; FAN Xiaoping; LI Zhetao; PAN Tian; CHOI Youngjune; SEKIYA Hiroo

doi:10.1049/cje.2016.01.013

Volume 25 Issue 1

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(1): 81-87

TIAN Shujuan, FAN Xiaoping, LI Zhetao, et al., “Orthogonal-Gradient Measurement Matrix Construction Algorithm,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 81-87, 2016, doi: 10.1049/cje.2016.01.013

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TIAN Shujuan, FAN Xiaoping, LI Zhetao, et al., “Orthogonal-Gradient Measurement Matrix Construction Algorithm,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 81-87, 2016, doi: 10.1049/cje.2016.01.013

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Orthogonal-Gradient Measurement Matrix Construction Algorithm

doi: 10.1049/cje.2016.01.013

1.
School of Information Science and Engineering, Central South University, Changsha 410075, China;
2.
The College of Information Engineering, Xiangtan University, Xiangtan 411105, China;
3.
Laboratory of Networked Systems, Hunan University of Finance and Economics, Changsha 410205, China;
4.
Department of Information and Computer Engineering, Ajou University, Suwon 443749, Korea;
5.
Graduate School of Advanced Integration Science, Chiba University, Chiba 263-8522, Japan

Funds: This work is supported by the National Natural Science Foundation of China (No.61379115, No.61110215, No.61311140261, No.61372049), Hunan Provincial National Natural Science Foundation of China (No.2015JJ4047, No.12JJ9021,No.13JJ8006), Hunan Provincial Science and Technology Project (No.2014GK3038), and the Construct Program of the Key Discipline in Hunan Province.

More Information

Corresponding author: LI Zhetao (corresponding author) is an associate professor and master supervisor in Xiangtan University. His main research interests include wireless network and compressive sensing. (Email: liztchina@hotmail.com)
Received Date: 2014-12-30
Rev Recd Date: 2015-09-18
Publish Date: 2016-01-10

Abstract

Abstract

An orthogonal-gradient measurement matrix construction algorithm is proposed for reducing the maximum and average mutual-coherence of sensing matrix. It shrinks Gram matrix based on equiangular tight frame theory. An orthogonal-gradient factor matrix is deduced. It obtains an optimized measurement matrix with the orthogonal-gradient factor matrix. The results of experiments show that the proposed algorithm effectively reduces the maximum and average mutual-coherence of sensing matrix. This leads to a better reconstruction performance for signals with different sparsities compared with Gaussian matrix, Elad's, Xu's, Vahid's and Li's methods.
- Compressive sensing (CS),
- Measurement matrix,
- Mutual-coherence,
- Equiangular tight frame

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References

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