Nguyen Thu L N, Shin Yoan
School of Electronic Engineering, Soongsil University, Seoul 156-743, Republic of Korea.
ScientificWorldJournal. 2013 Nov 5;2013:192795. doi: 10.1155/2013/192795. eCollection 2013.
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.
压缩感知是一种采样方法，它通过利用稀疏信号可以从极少的测量值中进行适当重构这一事实，为高效的信号压缩和恢复提供了一种新方法。压缩感知中最受关注的问题之一是感知矩阵的构造。虽然随机感知矩阵已得到广泛研究，但确定性感知矩阵却很少被考虑。这些矩阵在结构上非常理想，能够以减少存储需求的方式快速实现。本文对压缩感知的确定性感知矩阵进行了综述。我们介绍了压缩感知中的一个基本问题以及随机感知矩阵的一些缺点。讨论了关于确定性感知矩阵构造的一些最新成果。
