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Multichannel Classifier for Recognizing Acoustic Impacts Recorded with a phi-OTDR.

Ivan Alekseevich Barantsov1, Alexey Borisovich Pnev1, Kirill Igorevich Koshelev1

  • 1Photonics and Infra-Red Technology Scientific Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.

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|July 29, 2023
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Summary

This study introduces a machine learning algorithm for perimeter security, accurately detecting footsteps (98.3%) and distinguishing them from background noise (97.93%) using phase-sensitive optical time reflectometry (phi-OTDR) sensor data.

Keywords:
CNNOTDRacousticsartificial neural networksautoencoderclassificationmultichannel signal processingsignal processingskip connection

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Area of Science:

  • Security engineering
  • Signal processing
  • Machine learning

Background:

  • Perimeter security systems often struggle to reliably detect subtle intrusion signals like footsteps amidst background noise.
  • Phase-sensitive optical time reflectometry (phi-OTDR) offers a promising sensing technology for perimeter monitoring.

Purpose of the Study:

  • To develop an improved algorithm for classifying acoustic impacts to enhance perimeter security against unauthorized intrusions.
  • To increase the efficiency of perimeter detection tools by accurately identifying walking signals.

Main Methods:

  • A machine learning algorithm was developed using a dataset classifying footsteps versus non-footstep acoustic influences.
  • Real-time processing of space-time diagrams (grayscale images) generated from phi-OTDR data.
  • A three-channel neural network classifier, incorporating denoised autoencoder and adaptive correlation models, was employed for feature extraction and classification.

Main Results:

  • The algorithm achieved a 98.3% probability of correctly detecting footsteps.
  • Background actions were correctly classified with a 97.93% probability.
  • The system demonstrated effectiveness in distinguishing weak step signals from high-energy background noise.

Conclusions:

  • The developed algorithm significantly improves the accuracy of detecting footstep-based intrusions in perimeter security.
  • The integration of phi-OTDR with advanced machine learning techniques provides a robust solution for enhanced area security.