A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the sign...

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Bibliographic Details
Published in:IEEE access Vol. 6; pp. 4875 - 4894
Main Authors: Rani, Meenu, Dhok, S. B., Deshmukh, R. B.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acquisitions & mergers
Algorithms
Compressed sensing
Compressibility
Compressive sensing
CS acquisition strategies
CS applications
CS reconstruction algorithms
Domains
Image reconstruction
Magnetic resonance imaging
Mathematical model
OMP
random demodulator
Reconstruction
Reconstruction algorithms
Sensors
Sparse matrices
sparsity
Transforms
Ultrawideband
Video communication
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2793851