在宽域全光纤安防系统中,在不影响灵敏度的情况下区分入侵和正常事件是一个关键性的系统性能指标.由于入侵和正常事件以及各个入侵事件的光纤信号在某些情况下极为相似,因此需要对这些信号的特征进行仔细的筛选和识别.本文由此出发,提出了一套光纤信号特征提取方案,即使用小波降噪手段对光纤信号进行去噪;根据信号与噪声的能量在时域分布的不同,提出了一种实用的光纤信号的预分割方法;提取信号在小波空间的能量分布特征,形成特征向量;使用向量机分类器对光纤信号分类.

The discrimination between the intrusion and the nuisance events without compromising sensitivity is a key performance parameter for any outdoor perimeter intrusion detection system. In certain circumstances, the signals of the intrusion and the nuisance events are almost the same. This is especially the case for the intrusion and the nuisance events which may have a similar impact. In this paper, a series of methods are proposed to extract the fiber-signals: to denoise the input signal based on the wavelet transform; with a new practical algorithm of pre-segmentation of the fiber-signal, based on the hypothesis that the distribution of the energy of the intrusion in the time domain is different from that of the nuisance; to generate the eigenvector extracted from the distribution of the energy in the wavelet space; and to classify the fiber-signal using the support vector machine. With the assumption that different kinds of fiber-signals have different energy distributions in each frequency, the results of this experiment are satisfactory. This method is practical because we may take advantages of the SVM with very small cost.