NDLI: Sensing matrix based on Kasami codes for compressive sensing

Content Provider	IET Digital Library
Author	Nouasria, Hamid tolba, Mohamed Et
Abstract	Compressive sensing (CS) aims at acquiring sparse or compressible signals at a sampling rate much lower than Nyquist frequency. It allows for the original signal to be reconstructed from a small number of measurements. This involves an appropriate design of the sensing matrix to ensure signal recovery while reducing the number of measurements. In this study, the authors propose an improved deterministic Kasami sensing matrix whose columns have an enhanced orthogonality property. They demonstrate that the proposed matrix is suitable for existing recovery algorithms. Moreover, it outperforms the most existing sensing matrices in terms of the rate of exact reconstruction. In addition, it is shown that the use of the new sensing matrix for CS reduces significantly the memory requirement and the computational complexity.
Starting Page	1064
Ending Page	1072
Page Count	9
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 8, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0537
Journal	IET Signal Processing
Publisher Date	2018-06-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Compressed Sensing Compressible Signal Acquisition Compressive Sensing Computational Complexity Digital Signal Processing Enhanced Orthogonality Property Exact Reconstruction Rate Improved Deterministic Kasami Sensing Matrix Kasami Code Matrix Algebra Memory Requirement Sampling Rate Signal Detection Signal Reconstruction Signal Recovery Signal Sampling Sparse Signal Acquisition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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