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Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Qian Sun1, Hao Feng2, Xueying Yan3

  • 1Tianjin University, State Key Laboratory of Precision Measurement Technology & Instruments, 92 Weijin Road, Nankai District, Tianjin 300072, China. sunqiansohu@126.com.

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Summary

This study introduces a new time-space domain method for faster and more accurate intrusion event recognition in phase-sensitive optical time-domain reflectometer (Φ-OTDR) systems. The novel approach significantly enhances detection speed and accuracy for real-time vibration monitoring.

Keywords:
feature extractionintrusion event recognitionmorphologyΦ-OTDR

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Area of Science:

  • Optical Sensing
  • Signal Processing
  • Machine Learning for Cybersecurity

Background:

  • Traditional time-domain feature extraction in Φ-OTDR systems is slow and prone to noise, hindering real-time intrusion detection.
  • Current recognition speeds and accuracies do not meet the demands of online vibration monitoring applications.
  • Accurate and rapid intrusion event recognition is critical for the effective operation of Φ-OTDR sensing systems.

Purpose of the Study:

  • To develop a novel, efficient, and accurate feature extraction method for intrusion event recognition in Φ-OTDR systems.
  • To address the limitations of time-domain signal analysis in terms of speed and accuracy.
  • To enable reliable online vibration monitoring through improved intrusion detection.

Main Methods:

  • Utilized time-space domain signals for feature extraction, moving beyond traditional time-domain analysis.
  • Derived feature vectors from the morphologic characteristics of time-space domain signals.
  • Employed a scatter matrix for effective feature selection to optimize the recognition process.

Main Results:

  • Achieved a high recognition accuracy of 97.8% for intrusion events.
  • Reduced recognition time to below 1 second, demonstrating significant speed improvement.
  • The proposed method showed superior performance compared to traditional feature extraction techniques.

Conclusions:

  • The novel time-space domain feature extraction method significantly enhances recognition accuracy and speed in Φ-OTDR systems.
  • The method's efficiency and accuracy make it highly suitable for real-time online vibration monitoring.
  • This advancement offers a promising solution for improving the security and reliability of Φ-OTDR sensing applications.