DWT-CPLnet: A New Intrusion Disturbance Identification Paradigm for Optical Fiber Sensing Network in Open Environments

Perimeter security system based on distributed optical fiber sensor network plays a key role in the monitoring and protection of restricted areas and large industrial areas. At present, most of the distributed intrusion signal recognition algorithms rely on manual feature extraction methods and traditional classifiers such as traditional support vector machines, which generally have low recognition efficiency and accuracy. To solve these problems, a convolutional prototype network DWTCPLnet is proposed in this paper. Firstly, the original onedimensional intrusion interference signal is decomposed into five approximate coefficients in the frequency domain by discrete wavelet transform (DWT), and then combined with the original signal to form a new two-dimensional data. This two-dimensional data is then entered into DWT-CPLnet for training. At the same time, the training process of the network is restricted by the metric space of prototype learning. The experimental results indicate that the average recognition accuracy of DWT-CPLnet in 6 types of common intrusion disturbance signals (three natural disturbances: wind blowing, light rain, heavy rain; three manmade disturbances: knocking, impacting and slapping) can reach 99.59%, and also has the ability to identify unknown classes to meet the actual monitoring needs.