Maximum Eigenvalue-based detection in fiber-optic distributed acoustic sensors applications

Anomaly detection in large-scale time-series data acquired by Fiber Optic Distributed Acoustic Sensors (DAS) used for perimeter security and pipeline monitoring is a critical problem in machine learning. However, because of the vast amounts of data to process, it can be time and energy intensive. This study looks at how to reduce detection time and computing costs for this use case. In order to distinguish the acoustic event of interest from the noise and establish a binary detection threshold, we employ a Maximum Eigenvalue Detection (MED) approach in conjunction with a Random Matrix Theory (RMT) precept, namely the Tracy-Widom limit. A pipeline of signal processing techniques is used to assist the algorithm, beginning with applying a Moving Average (MA) filter to remove amplitude swings on the signal, which is represented by a data matrix, and then subsampling it to obtain uncorrelated signals among the subsequent columns to reduce the number of data processed. As a result, we can detect events of interest in less time. Following that, low-pass filtering is employed to eliminate low-frequency coefficients induced by various sorts of environmental and seismic events. Following normalization, the MED method is used to each of the Wishart matrices, which are generated by segmenting the data stream into equal small sub-matrices. RMT is used to set a threshold with a false alarm rate of 0.01 (FAR). The data columns matching to the selected MED values are then injected into a Convolutions Neural Network (CNN) to capture and detect the event of interest. When compared to using solely the CNN, the optimal results from our approach, MED followed by a CNN anomaly detection process, demonstrate a faster detection rate for events in security application.