Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition

Radmila Pribic; Geert Leus; C. Tzotzadinis

doi:10.1109/SSP.2018.8450796

Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition

Radmila Pribic, Geert Leus, C. Tzotzadinis

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

8 Downloads (Pure)

Abstract

Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance of diverse compressive data acquisition schemes by the output signal-to-noise ratio (SNR) they provide with the same input signal. The SNR is assessed analytically, and also confirmed numerically with simulated data. Only with a scheme of compressive data acquisition starting directly at reception (with no receiver noise yet), CS is less complicated and still performs as good as, if not better than, existing sensing.

Original language	English
Title of host publication	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	603-607
Number of pages	5
ISBN (Electronic)	978-1-5386-1570-3
ISBN (Print)	978-1-5386-1572-0
DOIs	https://doi.org/10.1109/SSP.2018.8450796
Publication status	Published - 2018
Event	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 10 Jun 2018 → 13 Jun 2018

Conference

Conference	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/Territory	Germany
City	Freiburg im Breisgau
Period	10/06/18 → 13/06/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

compressive sensing
data acquisition
performance
signal-to-noise ratio

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10.1109/SSP.2018.8450796

Signal-to-Noise-Ratio_Analysis_of_Compressive_Data_AcquisitionFinal published version, 947 KB

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title = "Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition",

abstract = "Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance of diverse compressive data acquisition schemes by the output signal-to-noise ratio (SNR) they provide with the same input signal. The SNR is assessed analytically, and also confirmed numerically with simulated data. Only with a scheme of compressive data acquisition starting directly at reception (with no receiver noise yet), CS is less complicated and still performs as good as, if not better than, existing sensing.",

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AU - Leus, Geert

AU - Tzotzadinis, C.

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N2 - Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance of diverse compressive data acquisition schemes by the output signal-to-noise ratio (SNR) they provide with the same input signal. The SNR is assessed analytically, and also confirmed numerically with simulated data. Only with a scheme of compressive data acquisition starting directly at reception (with no receiver noise yet), CS is less complicated and still performs as good as, if not better than, existing sensing.

AB - Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance of diverse compressive data acquisition schemes by the output signal-to-noise ratio (SNR) they provide with the same input signal. The SNR is assessed analytically, and also confirmed numerically with simulated data. Only with a scheme of compressive data acquisition starting directly at reception (with no receiver noise yet), CS is less complicated and still performs as good as, if not better than, existing sensing.

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KW - signal-to-noise ratio

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ER -

Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition

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Bibliographical note

Keywords

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