A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

Rui Wang; Jinglei Zhang; Suli Ren; Qingjuan Li

doi:10.1109/TST.2016.7399284

Tsinghua Science and Technology 2016, 21(1): 71-79 https://doi.org/10.1109/TST.2016.7399284

Open Access | Issue | Published: 04 February 2016

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

Show Author's Information Hide Author's Information Rui Wang(

), Jinglei Zhang, Suli Ren, Qingjuan Li

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.

Keywords:

wireless sensor networks, signal processing, compressed sensing

Cite this article:

Wang R, Zhang J, Ren S, et al. A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing. Tsinghua Science and Technology, 2016, 21(1): 71-79. https://doi.org/10.1109/TST.2016.7399284

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Abstract

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.

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A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

Show Author's information Hide Author's Information Rui Wang(

), Jinglei Zhang, Suli Ren, Qingjuan Li

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.

Abstract

Keywords: wireless sensor networks, signal processing, compressed sensing

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Acknowledgements

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Publication history

Received: 12 September 2015

Revised: 13 October 2015

Accepted: 02 November 2015

Published: 04 February 2016

Issue date: February 2016

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61379134) and by Fundamental Research Funds or the Central Universities (No. 06105031).