电子学报 ›› 2011, Vol. 39 ›› Issue (1): 142-148.

• 综述评论 • 上一篇    下一篇

压缩传感理论与重构算法

杨海蓉1,2, 张成1, 丁大为1, 韦穗1   

  1. 1. 安徽大学计算智能与信号处理教育部重点实验室,安徽合肥 230039;2. 合肥师范学院数学系,安徽合肥 230061
  • 收稿日期:2009-11-03 修回日期:2009-12-30 出版日期:2011-01-25 发布日期:2011-01-25

The Theory of Compressed Sensing and Reconstruction Algorithm

YANG Hai-rong1,2, ZHANG Cheng1, DING Da-wei1, WEI Sui1   

  1. 1. Key Laboratory of Intelligent Computing & Signal Processing,Anhui University,Hefei,Anhui 230039,China;2. Mathematics Department,Hefei Normal University,Hefei,Anhui 230061,China
  • Received:2009-11-03 Revised:2009-12-30 Online:2011-01-25 Published:2011-01-25

摘要: 压缩传感理论(Compressive Sensing,CS)以远低于Nyquist采样频率的非适应性测量和优化方法高概率重构信号.本文介绍了CS的基本理论、重构算法,包括贪婪、凸优化方法及我们提出的MBOOMP算法;同时,采用0-1组成的随机信号进行性能比较的模拟实验,结果表明我们的算法优于传统的OMP算法.

关键词: 压缩传感, 稀疏表示, 信号恢复, 无线传感网络, 模拟-信息转换

Abstract: Compressive sensing,by means of the non-adaptive measurements with a well below the Nyquist frequency and optimization methods,reconstruct signal with high probability.In this paper,we introduce the basic theory of compressed sensing and the main reconstruction algorithms,including iterative algorithms as well as our improved MBOOMP algorithm.Meanwhile,the simulation of radom signal which is composed of 0 and 1 are adapted to compare their performance.It is shown that our algorithm is better than typical OMP algorithm.

Key words: compressive sensing, sparse representation, signal recovery, wireless sensor networks, analog-to-information conversion

中图分类号: