Construction of a Class of Logistic Chaotic Measurement Matrices for Compressed Sensing

被引：4
|
作者：
Kong, Xiaoxue [1 ]
Bi, Hongbo [1 ]
Lu, Di [1 ]
Li, Ning [1 ]
机构：
[1] Northeast Petr Univ, Sch Elect & Informat Engn, Daqing, Peoples R China
来源：
PATTERN RECOGNITION AND IMAGE ANALYSIS | 2019年 / 29卷 / 03期
关键词：
compressed sensing; logistic chaos; correlation properties; chaos-Gaussian measurement matrix; RECONSTRUCTION;
D O I：
10.1134/S105466181903012X
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：493 / 502
页数：10
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