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Research and Software Design of an Φ-OTDR-Based Optical Fiber Vibration Recognition Algorithm

机译：基于φ-OTDR的光纤振动识别算法的研究与软件设计

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摘要

Distributed optical fiber vibration signal plays a significant role in the communication and safety of any perimeter security monitoring system. It uses light as an information carrier and optical fiber as a means of signal transmission and communication. Phase-sensitive optical time-domain reflectometry (phi-OTDR) is used to detect the signals generated during events (intrusions or nonintrusion). This paper proposes the time-frequency characteristic (TFC) method for the recognition of the fiber vibration signal and designs and implements the corresponding software function module. The combination of time-domain features and time-frequency-domain features is called TFC; and it is based on the Hilbert transform and on the empirical mode decomposition (EMD) of time-frequency entropy and center-of-gravity frequency that is described. A feature vector is formed, and multiple types of probabilistic neural networks (PNNs) are performed on it to determine whether intrusion events occur. The experimental simulation results show that the monitoring system software can intelligently display the data collected in real time, which demonstrates that the proposed method is effective and reliable in identifying and classifying accurately the types of events. The data processing time is less than 2 s, and the accuracy of the system identification can reach 99%, which ensures the system's validity.
机译：分布式光纤振动信号在任何周边安全监测系统的通信和安全性中起着重要作用。它用光作为信息载体和光纤，作为信号传输和通信的手段。相敏光学时域反射区（PHI-OTDR）用于检测事件期间产生的信号（入侵或非分解）。本文提出了用于识别光纤振动信号的时频特性（TFC）方法和设计，实现相应的软件功能模块。时域特征和时频域特征的组合称为TFC;并且它基于Hilbert变换，并在所述时频熵和重心频率的经验模式分解（EMD）上。形成特征向量，并且对其执行多种类型的概率神经网络（PNNS）以确定是否发生入侵事件。实验模拟结果表明，监控系统软件可以智能地显示实时收集的数据，这表明该方法在准确识别和分类事件的类型方面是有效和可靠的。数据处理时间小于2 s，系统识别的准确性可以达到99％，这确保了系统的有效性。

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  • 来源
    《Journal of electrical and computer engineering》 |2020年第1期|5720695.1-5720695.13|共13页
  • 作者

    Tabi Fouda Bernard Marie; Han Dezhi; An Bowen; Chen Xiangzhi;

  • 作者单位

    Shanghai Maritime Univ Dept Informat Engn Haigang Ave 1550 Shanghai 201306 Peoples R China;

    Shanghai Maritime Univ Dept Informat Engn Haigang Ave 1550 Shanghai 201306 Peoples R China;

    Shanghai Maritime Univ Dept Informat Engn Haigang Ave 1550 Shanghai 201306 Peoples R China;

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