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The Scientific World Journal
Volume 2013 (2013), Article ID 192795, 6 pages
http://dx.doi.org/10.1155/2013/192795
Research Article

Deterministic Sensing Matrices in Compressive Sensing: A Survey

School of Electronic Engineering, Soongsil University, Seoul 156-743, Republic of Korea

Received 5 August 2013; Accepted 30 September 2013

Academic Editors: Z. Cai, Y. Qi, and Y. Wu

Copyright © 2013 Thu L. N. Nguyen and Yoan Shin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed.