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

Conference paper (2018)

Authors

Radmila Pribic Thales Nederland B.V.

G.J.T. Leus Signal Processing Systems -

C. Tzotzadinis Student

Research Group

Signal Processing Systems () (TU Delft)

DOI: https://doi.org/10.1109/SSP.2018.8450796

Performance Data acquisition Compressive sensing Signal-to-noise ratio

To reference this document use:

http://resolver.tudelft.nl/uuid:8f362426-5fd4-4b03-8036-e1f2382f3b71

More Info

expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.

Files

Signal_to_Noise_Ratio_Analysis... (.pdf)

(.pdf | 1 Mb)

- Embargo expired in 02-03-2019