Distributed Optical Fiber Sensing Intrusion Pattern Recognition Based on GAF and CNN
Chengang Lyu, Ziqiang Huo, Xin Cheng, Jianying Jiang, Alimina Alimasi, Hongchen Liu
Abstract
This article proposes an intrusion pattern recognition scheme based on Gramian Angular Field (GAF) and convolutional neural network (CNN) for the dual Mach-Zehnder Interference (DMZI) distributed fiber perimeter security system, which has the advantages of fast recognition speed and high recognition accuracy rate. Compared with traditional recognition algorithms lack the ability to extract deep features, GAF algorithm is used to transform the intrusion signals of 1-D time series to 2-D images which can present more deep features and maintain the time domain dependence of the signals, meanwhile, each intrusion signal corresponds to a unique fingerprint. Then, this GAF algorithm combined with the appropriate CNN model is able to extract deep features and recognize the intrusion patterns accurately. Besides, GAF algorithm is insensitive to the fluctuation of power source in the optical path, so the robustness as well as the practicability of the system are improved effectively. The experiments demonstrated that the average recognition accuracy rate of three common natural intrusion events and three human intrusion events on fence (wind blowing, light rain, heavy rain, knocking, impacting and slapping) can reach to 97.67%, and the detection response time is only about 0.58 s, which can meet the requirements of real-time emergency monitoring.