Advances in Theory and Application of Compressed Sensing

SHI Guang-ming; LIU Dan-hua; GAO Da-hua; LIU Zhe; LIN Jie; WANG Liang-jun

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Advances in Theory and Application of Compressed Sensing

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- Advances in Theory and Application of Compressed Sensing
- Acta Electronica Sinica Vol. 37, Issue 5, Pages: 1070-1081(2009)
- 作者机构：
  
  1. 西安电子科技大学智能感知与图像理解教育部重点实验室,陕西,西安,710071
  2. 空军工程大学理学院,陕西,西安,710051
  3. 西北工业大学理学院,陕西,西安,710072
  4. 西安电子科技大学智能感知与图像理解教育部重点实验室陕西西安,710071
  5. 空军工程大学理学院陕西西安,710051
  6. 西北工业大学理学院陕西西安,710072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.72
- Published：2009
- 稿件说明：
移动端阅览
SHI Guang-ming, LIU Dan-hua, GAO Da-hua, et al. Advances in Theory and Application of Compressed Sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081.
DOI：

SHI Guang-ming, LIU Dan-hua, GAO Da-hua, et al. Advances in Theory and Application of Compressed Sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081. DOI：

摘要

信号采样是联系模拟信源和数字信息的桥梁.人们对信息的巨量需求造成了信号采样、传输和存储的巨大压力.如何缓解这种压力又能有效提取承载在信号中的有用信息是信号与信息处理中急需解决的问题之一.近年国际上出现的压缩感知理论(Compressed Sensing

CS)为缓解这些压力提供了解决方法.本文综述了CS理论框架及关键技术问题

并着重介绍了信号稀疏变换、观测矩阵设计和重构算法三个方面的最新进展

评述了其中的公开问题

对研究中现存的难点问题进行了探讨

最后介绍了CS理论的应用领域.

Abstract

Sampling is the bridge between analog source signal and digital signal.With the rapid progress of information technologies

the demands for information are increasing dramatically.So the existing systems are very difficult to meet the challenges of high speed sampling

large volume data transmission and storage.How to acquire information in signal efficiently is an urgent problem in electronic information fields.In recent years

an emerging theory of signal acquirement——compressed sensing (CS) provides a golden opportunity for solving this problem.This paper reviews the theoretical framework and the key technical problems of compressed sensing and introduces the latest developments of signal sparse representation

design of measurement matrix and reconstruction algorithm.Then this paper also reviews several open problems in CS theory and discusses the existing difficult problems.In the end

the application fields of compressed sensing are introduced.

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