Deep Learning Algorithms for Signal Recognition in Long Perimeter Monitoring Distributed Fiber Optic Sensors

TL;DR

This paper presents a deep learning-based approach for recognizing signals in distributed fiber optic sensors for long perimeter security, addressing challenges like error requirements and signal jamming.

Contribution

It introduces a two-level event detection architecture with an ensemble of deep convolutional networks for multiclass signal recognition in complex environments.

Findings

High accuracy in multiclass detection using real-life data

Robustness against signal jamming and environmental changes

Adaptability of the detection algorithms

Abstract

In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I and II) requirements and operate in difficult signal-jamming environments, with intensive signal-like jamming and a variety of changing possible signal portraits of possible recognized events. To address these issues, we have developed a twolevel event detection architecture, where the primary classifier is based on an ensemble of deep convolutional networks, can recognize 7 classes of signals and receives time-space data frames as input. Using real-life data, we have shown that the applied methods result in efficient and robust multiclass detection algorithms that have a high degree of adaptability.