Random Acquisition in Compressive Sensing: A Comprehensive Overview

Mahdi Khosravy, Thales Wulfert Cabral, Max Mateus Luiz, Neeraj Gupta and Ruben Gonzalez Crespo
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Mahdi Khosravy: Osaka University, Japan
Thales Wulfert Cabral: State University of Campinas, Brazil
Max Mateus Luiz: Federal University of Juiz de Fora, Brazil
Neeraj Gupta: Oakland University, USA
Ruben Gonzalez Crespo: Universidad Internacional de La Rioja, Spain

International Journal of Ambient Computing and Intelligence (IJACI), 2021, vol. 12, issue 3, 140-165

Abstract: Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

Date: 2021
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