Accurately detecting man-made intrusion from different events is of great significance for distributed optical fiber sensing perimeter security system. Most traditional algorithms lack the ability to reject various events of unknown class which are mainly from natural disturbance, and greatly decline the accuracy of intrusion recognition in field application. In order to solve this problem, we proposed a novel robust intrusion event recognition scheme based on convolutional prototype network (CPL), which realized end-to-end feature extraction and recognition based on the similarity of intrusion signals by integrating relevant variables of prototype learning into the training process of multiscale convolutional neural network (MSCNN) as trainable parameters, and had the ability to recognize and reject the unknown disturbance events. In field experiments, the average recognition accuracy of intrusion events as known class can reach 84.67%, with the rejection rate of disturbance events as unknown class is about 83.75%, which ensure the accuracy of intrusion events monitoring in complex field environments. And the recognition response time is about 17 ms, which also meets the need of real-time monitoring.

中文翻译：

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分布式光纤传感周界安全系统的鲁棒入侵事件识别方法

准确检测来自不同事件的人为入侵对于分布式光纤传感周界安防系统具有重要意义。大多数传统算法缺乏对以自然干扰为主的各种未知类事件的拒绝能力，在现场应用中大大降低了入侵识别的准确性。为了解决这个问题，我们提出了一种新颖的基于卷积原型网络（CPL）的鲁棒入侵事件识别方案，通过整合原型学习的相关变量，实现了基于入侵信号相似性的端到端特征提取和识别。进入多尺度卷积神经网络（MSCNN）的训练过程作为可训练参数，并具有识别和拒绝未知干扰事件的能力。在现场实验中，已知类入侵事件的平均识别准确率可达84.67%，未知类干扰事件的拒绝率约为83.75%，保证了复杂现场环境下入侵事件监测的准确性。并且识别响应时间在17ms左右，也满足了实时监控的需要。