Utilizing the sparsity of quasi-distributed sensing systems for sub-Nyquist signal reconstruction

Lihi Shiloh; Raja Giryes; Avishay Eyal

doi:10.1117/12.2541252

28 August 2019 Utilizing the sparsity of quasi-distributed sensing systems for sub-Nyquist signal reconstruction

Lihi Shiloh, Raja Giryes, Avishay Eyal

Proceedings Volume 11199, Seventh European Workshop on Optical Fibre Sensors; 111992F (2019) https://doi.org/10.1117/12.2541252
Event: Seventh European Workshop on Optical Fibre Sensors, 2019, Limassol, Cyprus

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.

Citation Download Citation

Lihi Shiloh, Raja Giryes, and Avishay Eyal "Utilizing the sparsity of quasi-distributed sensing systems for sub-Nyquist signal reconstruction", Proc. SPIE 11199, Seventh European Workshop on Optical Fibre Sensors, 111992F (28 August 2019); https://doi.org/10.1117/12.2541252

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