Fiber-Optic Distributed Acoustic Sensing (DAS) intrusion detection systems provide effective solutions for the border, critical infrastructure, facility, and pipeline security applications. DAS systems are able to detect and classify acoustic vibrations using standard telecommunication fibers buried under the ground or deployed over a fence. Activities of interest captured by the DAS system may not pose the same level of threat depending on the time and location of the activity. For instance, a ground digging activity during the day time in rural areas close to a pipeline is more likely an agricultural event rather than a suspicious illegal tapping on the pipeline. These everyday events can be misleading if the operator is notified with the same audio-visual alarms for both cases and may create frustration in the operator. Therefore assigning threat levels to the activities is an essential feature of the DAS systems to increase their credibility. In this paper, we propose a threat level assessment method that learns the activity density of the area in an unsupervised manner. Activities are scored using a threat metric and they are assigned levels using a novel dynamic thresholding approach.
This paper presents a distributed acoustic sensing based linear asset protection system along with novel signal processing and threat classification techniques. The sensing system is realized by direct detection phase-OTDR (optical time domain reflectometry). An effective signal preprocessing approach for noise reduction that aims to improve the threat detection capability of the system is proposed. The proposed method is not limited to direct detection based systems and is applicable to any phase-OTDR system. A novel deep learning based threat clas- sification method is presented to identify various types of threats. The method uses a deep convolutional neural network trained with real sensor data. Experiments are conducted with an ITU-T G.652 fiber optic cable buried at one meter depth. The effects of applied preprocessing methods on both threat detection and threat classification performance are analyzed. The proposed preprocessing method is compared with the methods commonly used in the literature such as time differencing and wavelet denoising. The results show that by applying the proposed signal conditioning, event detection and classification methods, threat classification accuracies above 93% can be achieved with six typically observed activities, namely, walking, digging with pickaxe, digging with shovel, digging with harrow, strong wind and facility noise caused by water pipes, generators or air conditioning, at ranges of up to 40 km. The proposed classification strategy can easily be generalized for identifying different types of threats that are of interest in various security applications.
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