Φ -OTDR Event Recognition System Based on Reconstructed Signal Feature Space

In Internet of Things, V-OTDR distributed fiber optic sensors are often used to collect and monitor event signals. Recently, deep learning models have been widely used in V -OTDR event recognition systems. However, traditional event signal classification models only can recognize known events, and they identify known events by constructing class boundaries without intra-class compactness. A signal feature space method for recognizing V -OTDR events is proposed in this paper. This method can not only effectively construct class boundaries, but also reduce intra-class distances. The more accurate class boundaries will help to reject the unknown class samples, which will inversely improve classification accuracy of unknown classes. The experimental results show that the proposed method has an accuracy of 91.851% in recognizing known events and 90.314 in recognizing unknown events, with an average recognition accuracy improvement of 13.6% compared to traditional methods.