In this paper, a fiber optic based acoustic sensing system (FOTAS) is presented. Utilizing such cables as hundreds of acoustic movement detectors has become a novel approach for many security applications. Developed for securing critical infrastructures via utilizing both buried and fence-mounted cables, phase OTDR system FOTAS’s requirements, design, development, test and analysis modules are outlined. The detected movement type, location, magnitude are conveyed to the user via a user-friendly interface. Associated hardware, software, and utilized fiber optic network characteristics are presented. Detailed information about developed novel signal processing and artificial intelligencebased movement detection studies are provided. Real world data indicate that preliminary threat detection performance is more than 90%.
This work aims at the detection and classification of Distributed Acoustic Sensor (DAS) acquired acoustic signals. We obtained the data by probing an optical fiber with light pulses and gauging the Rayleigh backscatter. Said data contains four different classes; Walking, Shovel and Pick digging as well as Hammer hitting. We first proceed by detecting the event and its location along the fiber and extracting it from the random noise using Spiked Random Matrix Theory (RMT) models, namely Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions. We then label the datasets accordingly and proceed with the classification process using machine learning algorithms. For this, we test and evaluate Convolutional Neural Networks (CNN), which has been proven to provide high accuracies in similar studies, taking the spectrograms of the signals as our network’s input. We conclude by providing the performance of our CNN architecture and propose a few options to further improve the performance of the model.
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