Improved Perturbation Detection in Direct Detected φ-OTDR Systems using Matched Filtering

Muhammad Adeel; Javier Tejedor; Javier Macias-Guarasa; Chao Lu

doi:10.1109/LPT.2019.2940297

Improved Perturbation Detection in Direct Detected φ-OTDR Systems using Matched Filtering

Muhammad Adeel, Javier Tejedor, Javier Macias-Guarasa, Chao Lu

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

12 Citations (Scopus)

Abstract

Nuisance Alarm Rate (NAR) is critical in φ-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forest-based machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications.

Original language	English
Article number	8830461
Pages (from-to)	1689-1692
Number of pages	4
Journal	IEEE Photonics Technology Letters
Volume	31
Issue number	21
DOIs	https://doi.org/10.1109/LPT.2019.2940297
Publication status	Published - 1 Nov 2019

Keywords

Distributed acoustic sensing
perturbation detection
phase-OTDR

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering

More information

10.1109/LPT.2019.2940297

http://hdl.handle.net/10397/95334

Cite this

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title = "Improved Perturbation Detection in Direct Detected φ-OTDR Systems using Matched Filtering",

abstract = "Nuisance Alarm Rate (NAR) is critical in φ-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forest-based machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications.",

keywords = "Distributed acoustic sensing, perturbation detection, phase-OTDR",

author = "Muhammad Adeel and Javier Tejedor and Javier Macias-Guarasa and Chao Lu",

note = "Funding Information: Manuscript received July 31, 2019; revised August 28, 2019; accepted September 4, 2019. Date of publication September 10, 2019; date of current version November 12, 2019. This work was supported in part by the Spanish Ministry of Economy and Competivity under Project TIN2016-75982-C2-1-R, and in part by the General Research Fund (GRF: PolyU 152658/16E, 152168/17E). (Corresponding author: Muhammad Adeel.) M. Adeel and C. Lu are with the Department of Electronic Engineering, Photonics Research Centre, The Hong Kong Polytechnic University (PolyU), Hong Kong (e-mail: [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2019 IEEE.",

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AU - Tejedor, Javier

AU - Macias-Guarasa, Javier

AU - Lu, Chao

N1 - Funding Information: Manuscript received July 31, 2019; revised August 28, 2019; accepted September 4, 2019. Date of publication September 10, 2019; date of current version November 12, 2019. This work was supported in part by the Spanish Ministry of Economy and Competivity under Project TIN2016-75982-C2-1-R, and in part by the General Research Fund (GRF: PolyU 152658/16E, 152168/17E). (Corresponding author: Muhammad Adeel.) M. Adeel and C. Lu are with the Department of Electronic Engineering, Photonics Research Centre, The Hong Kong Polytechnic University (PolyU), Hong Kong (e-mail: [email protected]; [email protected]). Publisher Copyright: © 2019 IEEE.

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N2 - Nuisance Alarm Rate (NAR) is critical in φ-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forest-based machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications.

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KW - Distributed acoustic sensing

KW - perturbation detection

KW - phase-OTDR

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Improved Perturbation Detection in Direct Detected φ-OTDR Systems using Matched Filtering

Abstract

Keywords

ASJC Scopus subject areas

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