Construction of a Class of Logistic Chaotic Measurement Matrices for Compressed Sensing
- 作者: Kong X.1, Bi H.1, Lu D.1, Li N.1
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隶属关系:
- School of Electrical and Information Engineering, Northeast Petroleum University
- 期: 卷 29, 编号 3 (2019)
- 页面: 493-502
- 栏目: Applied Problems
- URL: https://journals.rcsi.science/1054-6618/article/view/195655
- DOI: https://doi.org/10.1134/S105466181903012X
- ID: 195655
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详细
The construction of the measurement matrix is the key technology for accurate recovery of compressed sensing. In this paper, we demonstrated correlation properties of nonpiecewise and piecewise logistic chaos system to follow Gaussian distribution. The correlation properties can generate a class of logistic chaotic measurement matrices with simple structure, easy hardware implementation and ideal measurement efficiency. Specifically, spread spectrum sequences generated by the correlation properties follow Gaussian distribution. Thus, the proposed algorithm constructs chaos-Gaussian matrices by the sequences. Simulation results of one-dimensional signals and two-dimensional images show that chaos-Gaussian measurement matrices can provide comparable performance against common random measurement matrices. In addition, chaos-Gaussian matrices are deterministic measurement matrices.
作者简介
Xiaoxue Kong
School of Electrical and Information Engineering, Northeast Petroleum University
Email: wdskxx@126.com
中国, Daqing
Hongbo Bi
School of Electrical and Information Engineering, Northeast Petroleum University
编辑信件的主要联系方式.
Email: wdskxx@126.com
中国, Daqing
Di Lu
School of Electrical and Information Engineering, Northeast Petroleum University
Email: wdskxx@126.com
中国, Daqing
Ning Li
School of Electrical and Information Engineering, Northeast Petroleum University
Email: wdskxx@126.com
中国, Daqing
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