在分布式光纤周界安防系统中, 对距离较长、背景环境复杂的边境进行检测时, 系统根据光缆沿线发生的事件进行识别。为了区分各种引起光缆振动的激励, 本文根据入侵信号与环境引起的振动信号在小波尺度上方差幅值的分布特征, 利用小波多尺度分析理论构造了由各尺度下的方差组成的特征向量, 提出了根据方差特征向量的不同来识别各种振动信号的“尺度－方差”信号的方法。在实验系统中, 光缆总长度为 56 km, 光源的功率为 300 μW, 工作波长为 1 550 nm。实验结果表明, 此方法可以有效区分入侵信号、环境噪声和人为活动引起的非入侵事件, 提高了系统的检测概率和降低系统的虚警率。

In an optical fiber perimeter security system, the longer distance and background environment complex border detecting, the system based on optical fiber cable line implements the event recognition. In order to distinguish the various induced cable vibration excitations, according to the intrusion signal and environment caused by vibration signal in wavelet scale above difference amplitude distribution characteristics, by using wavelet multi-resolution analysis theory, the scale variance component of feature vectors is constructed. According to the variance feature vector, scale- variance signal method for identifying different kinds of vibration signals is proposed. In the experiment system, total cable length is 56 km, the power of the light source of 300 μW, working at a wavelength of 1 550 nm. The experimental results show that the method can effectively distinguish the intrusion signal noise, environment and human activities caused by non -invasion, improve the detection probability and reduce the false alarm rate.