摘要
压缩感知是针对稀疏或可压缩信号,在采样的同时即可对信号数据进行适当压缩的新理论,采用该理论,可以仅需少量信号的观测值来实现精确重构信号。文中概述了CS理论框架及关键技术问题,介绍了信号稀疏表示、观测矩阵和重构算法。最后仿真实现了基于压缩感知的信号重构,并对正交匹配追踪(OMP)重构算法性能作了分析。
Compressive sensing （CS）is a novel signal sampling theory under the condition that the signal is sparse or compressible.It has the ability of compressing a signal during the process of sampling.Using compressive sensing theory,one can reconstruct sparse or compressible signals accurately from a very limited number of measurements. This paper surveys the theoretical framework and the key technical problems of compressed sensing and introduces signal sparse representation, measurement matrix and reconstruction algorithms. In the end, realizes signal reconstruction and analyses the performances of Orthogonal Matching Pursuit（OMP）reconstruction algorithms.
出处
《电子设计工程》
2013年第7期34-36,40,共4页
Electronic Design Engineering
基金
江苏科技大学本科生创新计划专项经费资助(103022005)
