Compressive Sensing Using Singular Value Decomposition

被引：0

作者：

Xu, Lei ^{[1
]}

Liang, Qilian ^{[1
]}

机构：

[1] Univ Texas Arlington, Dept Elect Engn, Arlington, TX 76013 USA

来源：

WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS | 2010年 / 6221卷

关键词：

compressive sensing; Singular Value Decomposition; sparse basis;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Using Singular Value Decomposition (SVD), we develop an algorithm for signal recovery in compressive sensing. If the signal or sparse basis is properly chosen, an accurate estimate of the signal could be obtained by a simple and efficient signal recovery method even in the presence of additive noise. The theoretical and simulation results show that our approach is scalable both in terms of number of measurements required for stable recovery and computational complexity.

引用

页码：338 / 342

页数：5

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