摘要
分布式光纤周界安防系统被证明是对发电厂、变电站和电信基站等重要目标进行安全监控的有效方法,然而,这种方法很难区分不同类别的入侵行为,容易被不同种类的环境干扰误触发报警.伴随着实际需求的提升,对于周界信号识别精度有了更高的要求,新时代的周界安防系统不仅需要对各类入侵行为进行实时监控、识别和响应报警,同时还要兼具远程控制与响应、高精度入侵定位、多环境适应性、抗各种扰动和低能耗等特性.因此对入侵信号特征的有效提取及准确识别算法的研究很有必要.文章综述了光纤周界信号时域、频域及时频域结合的特征提取方法,基于向量机、神经网络及深度学习等分类识别方法,具体讨论了各类算法的原理及应用场合,并对各类算法的优劣性能进行了对比分析.
Abstract
Distributed optical fiber perimeter security systems have proven to be an effective method for security monitoring of important targets such as power plants,substations,and telecommunications base stations.However,this method can be challenging to distinguish between different types of intrusion behaviors and is prone to false alarms triggered by various envi-ronmental interferences.With the increasing actual demand,there are higher requirements for the accuracy of perimeter signal recognition.The perimeter security system in the new era not only needs to perform real-time monitoring,recognition,and re-sponse alarms for various types of intrusion behaviors,but also requires features such as remote control and response,high-precision intrusion location,multi-environmental adaptability,resistance to various disturbances,and low energy consumption.Therefore,it is necessary to conduct research on effective extraction and accurate recognition algorithms for intrusion signal characteristics.This article reviews the feature extraction methods combining the time domain,frequency domain,and time-frequency domain of optical fiber perimeter signals,and the classification and recognition methods based on vector machines,neural networks,and deep learning.It specifically discusses the principles and application scenarios of various algorithms,and conducts a comparative analysis of their advantages and disadvantages.
维普
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