A reducing iteration orthogonal matching pursuit algorithm for compressive sensing

被引：0

作者：

Wang R. ^{[1
]}

Zhang J. ^{[2
]}

Ren S. ^{[1
]}

Li Q. ^{[1
]}

机构：

[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing

[2] School of Computer and Control Engineering, University of Chinese, Academy of Sciences, Beijing

来源：

Wang, Rui (wangrui@ustb.edu.cn) | 1600年 / Tsinghua University卷 / 21期

关键词：

compressed sensing; signal processing; Wireless sensor networks;

D O I：

10.1109/TST.2016.7399284

中图分类号：

学科分类号：

摘要：

In recent years, Compressed Sensing (CS) has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit (OMP) is fixed, thus limiting reconstruction precision. Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit (RIOMP) algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations. The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses. When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time. © 1996-2012 Tsinghua University Press.

引用

页码：71 / 79

页数：8

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