An Interferometric Optical Fiber Perimeter Security System Based on Multi-Domain Feature Fusion and SVM

Abstract

In this paper, an interferometric optical fiber perimeter security system based on multi-domain feature fusion and support vector machine (SVM) is reported. To improve the intrusion event classification accuracy and reduce the overall cost, advanced optical fiber sensor technique and data analysis method are synthesized to build the system. For the optical component, the Michelson interferometer sensor structure and the rectangular-pulse binary phase modulation method are adopted to reduce the system complexity and cost. For the electronic data analysis component, mixed domains (including time, frequency and wavelet domains) features are selected and the SVM is adopted to classify the intrusion event type. Specifically, the zero-crossing rate (ZCR) is chosen as the feature element in the time domain, whose threshold is determined by analyzing the environmental noise. The variance of the power spectral density (PSD) of the phase signal in four frequency bands are selected as the feature element in the frequency domain. The energy of the phase signal by using the wavelet packet decomposition in four full frequency bands is chosen as the feature element in the wavelet domain. All the acquired features corresponding to various event conditions are combined into the feature vector data set, which are trained and predicted by the SVM. In the field experiment test, the proposed identification scheme was used to identify and classify five kinds of events, including non-intrusion, climbing, shaking, iron bar knocking and fiber cable shearing. The achieved average classification accuracy reached 94.4%.