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Published in: Pattern Recognition and Image Analysis 3/2019

01-07-2019 | APPLIED PROBLEMS

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

Authors: Xiaoxue Kong, Hongbo Bi, Di Lu, Ning Li

Published in: Pattern Recognition and Image Analysis | Issue 3/2019

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Abstract

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.

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Metadata
Title
Construction of a Class of Logistic Chaotic Measurement Matrices for Compressed Sensing
Authors
Xiaoxue Kong
Hongbo Bi
Di Lu
Ning Li
Publication date
01-07-2019
Publisher
Pleiades Publishing
Published in
Pattern Recognition and Image Analysis / Issue 3/2019
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI
https://doi.org/10.1134/S105466181903012X

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