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相位敏感光时域反射系统模式识别方法综述 被引量:11

Summarization of pattern recognition method for phase sensitive optical time domain reflecting system
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摘要 基于相位敏感的光时域反射系统(Ф-OTDR)是一种新型的分布式光纤扰动传感系统。随着应用需求的不断细化,单纯对外部侵扰活动的检测及定位已无法满足实际需要,亟待对检测到的信号进行准确的分类识别。在检测到侵扰信号的同时,如何能准确判别入侵事件的类别,减少误报率和漏报率是分布式光纤扰动传感系统研究的关键问题。文中主要针对分布式光纤扰动传感系统的原理进行了简要的介绍,将现有的扰动信号特征提取的方法和分类器设计的方法进行归纳和分类,并对识别结果进行总结和对比以方便研究人员根据应用环境的差异以及待测信号的特征,准确选择适合的信号模式识别方法,促进研究人员对分布式光纤扰动传感系统模式识别方法进行更为深入的研究。 Phase-sensitive optical time domain reflection system( Ф-OTDR) is a new type of distributed fiber perturbation sensing system. With the continuous improvement of application requirements, the detection and positioning of external intrusion can not satisfy the actual needs, the accurate detection of the signal identification is urgently needed. When detecting the intrusion signal, it is a key problem in the research of distributed optical fiber disturbance sensor system how to accurately distinguish the classification of intrusion events and reduce the false alarm rate and false negative rate. The principle of distributed fiber perturbation sensing system was briefly introduced, the existing methods of feature extraction and classifier design were summarized and classified, and the recognition results were summarized and compared to facilitate the research. According to the difference of the application environment and the characteristics of the signal to be measured, the accurate selection of the appropriate signal pattern recognition method is adopted to promote the researchers to study the pattern recognition method of distributed fiber perturbation sensing system more deeply.
作者 付群健 于淼 常天英 张瑾 罗政纯 王旭 刘珉含 崔洪亮 Fu Qunjian;Yu Miao;Chang Tianying*;Zhang Jin;Luo Zhengchun;Wang Xu;Liu Minhan;Cui Hongliang(College of Instrumentation & Electrical Engineering,Jilin University,Changchun 130012,China)
出处 《红外与激光工程》 EI CSCD 北大核心 2018年第7期266-279,共14页 Infrared and Laser Engineering
基金 海洋公益性行业科研专项(201405026-01)
关键词 分布式光纤扰动 模式识别 特征提取 分类器设计 distributed fiber perturbation pattern recognition feature extraction classifier design
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