Intrusion Event Identification Approach for Distributed Vibration Sensing Using Multimodal Fusion

Abstract

Perimeter security intrusion monitoring, relying on distributed optical fiber vibration sensing (DVS) systems, is prevalent, yet effectively identifying vibration signals remains a challenge. This article introduces a method for intrusion event pattern recognition based on deep learning (DL) and multimodal feature fusion, facilitating automatic feature extraction and fusion. The approach integrates 1-D raw time-domain signals (1D-RTD-signal) and 2-D time-frequency spectrum (2D-TF-spectrum) as training data. While the 1D-RTD-signal preserves comprehensive raw data information, the 2D-TF-spectrum unveils time-frequency characteristics of the vibration signal. Specifically, the 1DCNN-DN multimodal feature fusion model is tailored for this purpose. Initially, the branch model independently extracts features, followed by fusion for recognition. Leveraging original time-domain signals streamlines data preprocessing, and the multimodal fusion model enhances model generalization while averting overfitting. Experimental outcomes showcase the scheme's prowess in recognizing background noise and three types of intrusion events in outdoor environments, achieving an average recognition rate of 99.36%. This presents an optimized solution for DVS perimeter security technology, facilitating the classification and recognition of vibration signals from various intrusion events.