CAJ | 학술논문

针对分布式光纤在周界安防系统中信号种类，即不同的环境下产生的噪声信号干扰和常见的入侵产生的信号。本文基于全光纤马赫-泽德干涉仪的分布式光纤传感模型，提出了一种识别常见的越境信号和消除环境噪声干扰信号的方法，实现了在去除环境干扰的情况下，用 BP 神经网络对多种入侵信号识别。实验结果证明，该方法能够有效的区分越境信号和不同环境状态产生的噪声信号，极大的提高了整个系统的识别率，降低了其虚警率。
침대분포식광섬재주계안방계통중신호충류，즉불동적배경하산생적조성신호간우화상견적입침산생적신호。본문기우전광섬마혁-택덕간섭의적분포식광섬전감모형，제출료일충식별상견적월경신호화소제배경조성간우신호적방법，실현료재거제배경간우적정황하，용 BP 신경망락대다충입침신호식별。실험결과증명，해방법능구유효적구분월경신호화불동배경상태산생적조성신호，겁대적제고료정개계통적식별솔，강저료기허경솔。
The signal source that may be found in perimeter security system of distributed Fiber-optic includes noise signals caused by different environmental state and intrusion signals caused by common kinds of invasion. In order to distinguish noise signals from intrusion signals, a new method based on the M-Z interferometers is proposed. In addition, by using BP neural network, we can recognize different targets while a variety of invasive signals through the Fiber-optic without any ambient noises. Experiment results show that the proposed method is able to differentiate intrusion signals from ambient noises effectively. What’s more, the recognition rate of the system is improved while the false alarm rate is reduced.