There is an increasing interest among the researcher community, and industries on the design, and development of a combined distributed acoustic sensing, and a pattern recognition system to detect, and classify potentially dangerous intrusion events. In this work, we describe the design, and optimization of a distributed intrusion sensing system using Rayleigh-phase sensitive optical time domain reflectometry (Rayleigh Φ -OTDR) technique, and supervised machine learning algorithms. The proposed system can classify an intrusion along with the position of an intrusion caused along a single mode optical fiber. We have considered three different external intrusion events, such as a person walking, digging by pickaxe, and electrical drilling. After training and testing the data samples of the simulated intrusion events, we have achieved an average intrusion classification rate of 100% with a 10 dBm of input laser source power over a 25 km length of sensing fiber. The relevant simulated experiments are carried out using MATLAB 20.0 platform.

研究人员社区和行业之间对组合分布式声感测以及用于识别和分类潜在危险入侵事件的模式识别系统的设计和开发越来越感兴趣。在这项工作中，我们描述了使用瑞利相位敏感光时域反射仪（RayleighΦ-OTDR）技术和监督机器学习算法的分布式入侵感测系统的设计和优化。提出的系统可以将入侵以及沿着单模光纤引起的入侵的位置分类。我们考虑了三种不同的外部入侵事件，例如一个人走路，用镐挖洞和电钻。在训练和测试了模拟入侵事件的数据样本之后，我们在25公里长的传感光纤上以10 dBm的输入激光源功率实现了100％的平均入侵分类率。相关的仿真实验是使用MATLAB 20.0平台进行的。