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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 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 languageEnglish
Article number8830461
Pages (from-to)1689-1692
Number of pages4
JournalIEEE Photonics Technology Letters
Volume31
Issue number21
DOIs
Publication statusPublished - 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

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