Construction of Measurement Matrix in Compressive Sensing Based on Composite Chaotic Mapping

ZHOU Wei; JING Bo; ZHANG Hang; HUANG Yi-feng; LI Juan

doi:10.3969/j.issn.0372-2112.2017.09.018

您当前的位置：

首页 >

文章列表页 >

Construction of Measurement Matrix in Compressive Sensing Based on Composite Chaotic Mapping

更新时间：2025-07-16

- Construction of Measurement Matrix in Compressive Sensing Based on Composite Chaotic Mapping
- Acta Electronica Sinica Vol. 45, Issue 9, Pages: 2177-2183(2017)
- 作者机构：
  
  1. 北京机电工程研究所,北京,100074
  2. 空军工程大学航空航天工程学院,陕西,西安,710038
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2017.09.018
  CLC： TP212.9
- Published Online：25 September 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
ZHOU Wei, JING Bo, ZHANG Hang, et al. Construction of Measurement Matrix in Compressive Sensing Based on Composite Chaotic Mapping[J]. Acta Electronica Sinica, 2017, 45(9): 2177-2183.
DOI：

ZHOU Wei, JING Bo, ZHANG Hang, et al. Construction of Measurement Matrix in Compressive Sensing Based on Composite Chaotic Mapping[J]. Acta Electronica Sinica, 2017, 45(9): 2177-2183. DOI： 10.3969/j.issn.0372-2112.2017.09.018.

摘要

针对常用随机测量矩阵存在硬件实现困难的不足，提出一种基于复合混沌映射的压缩感知确定性测量矩阵构造方法.首先基于Logistic映射和Tent映射构造随机性和初值敏感性更强的复合混沌映射，然后将复合混沌迭代序列经大间隔采样后进行线性变换得到的结果作为拟构造测量矩阵中的元素，并从理论上证明了该矩阵元素具有非常低的相关性.同时理论证明了所构造复合混沌测量矩阵能以高概率满足压缩感知约束等距性.实验结果表明，所构造复合混沌测量矩阵的性能优于Toeplitz测量矩阵及Logistic映射测量矩阵，与高斯随机测量矩阵的性能相仿.

Abstract

Aiming at the difficult in hardware realization of random measurement matrix

we construct a deterministic measurement matrix based on composite chaotic mapping.The composite chaotic mapping that based on Tent mapping and Logistic mapping

has stronger randomicity and initial value sensitivity.Sampled the composite chaotic mapping sequence with large distance

and then do linear transformation to the sampling sequence.Finally

we construct the measurement matrix with the linear transformation result.We prove that the measurement matrix elements have enough statistically independent

and the measurement matrix can satisfies restricted isometry property (RIP) with large probability.The simulation result shows that our matrix has the similar performance to Gaussian random matrix

and better than Toeplitz deterministic measurement matrix and Logistic deterministic measurement matrix.

关键词

Keywords

references

Views

356

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Robust Image Watermarking Based on Chaotic Mapping

Research on Recovery of Clipping and HPA Nonlinear Distortion Based on Compressive Sensing in OFDM Systems

A Construction Algorithm of Measurement Matrix with Low Power Average Column Coherence

Collaborative Construction of Measurement Matrix and Reconstruction Algorithm in Compressive Sensing

Mutual Support Dual Watermark Algorithm Based on Compressive Sensing

Related Author

ZHANG Chun-tian

ZHANG Jing

YANG Lin

SONG Kun

LI Zhe-tao

PAN Tian

ZHU Geng-ming

PEI Ting-rui

Related Institution

School of Electronic Information Engineering, Tianjin University

Key Laboratory of National Communication Technology, University of Electronic Science and Technology

Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, The 54 th Research Institute of China Electronics Technology Group Corporation

Key Laboratory of National Communication Technology University of Electronic Science and Technology Chengdu Sichuan China

Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory The 54 th Research Institute of China Electronics Technology Group Corporation Shijiazhuang Hebei China

⁰