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Utilizing the sparsity of quasi-distributed sensing systems for sub-Nyquist signal reconstruction
Author(s): Lihi Shiloh; Raja Giryes; Avishay Eyal
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Paper Abstract

Quasi-distributed sensing, e.g. Quasi-Distributed Acoustic Sensing (Q-DAS), with optical fibers is commonly used for various applications. Its excellent performance is well known, however, it necessitates high sampling rates and expensive hardware for acquisition and processing. In this paper, we introduce a technique, based on Compressed Sensing (CS) theory, to locate discrete reflectors' along a sensing fiber with a smaller number of samples than required according to Nyquist criterion. The technique is based on the fact that the fiber profile consists of a limited number of discrete reflectors and significantly weaker reflections of Rayleigh back-scatterers, and thus can be approximated as a sparse signal. The task of reconstructing a sparse signal from a sub-Nyquist sampled signal using Orthogonal Matching Pursuit (OMP) is presented and tested experimentally.

Paper Details

Date Published: 28 August 2019
PDF: 4 pages
Proc. SPIE 11199, Seventh European Workshop on Optical Fibre Sensors, 111992F (28 August 2019); doi: 10.1117/12.2541252
Show Author Affiliations
Lihi Shiloh, Tel Aviv Univ. (Israel)
Raja Giryes, Tel Aviv Univ. (Israel)
Avishay Eyal, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 11199:
Seventh European Workshop on Optical Fibre Sensors
Kyriacos Kalli; Sinead O. O'Keeffe; Gilberto Brambilla, Editor(s)

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