基于高灵敏度光纤布喇格光栅振动传感器，提出了一种光纤围栏入侵监测系统及其模式识别方法.该方法通过具有自适应动态阈值的时域统计特征提取算法对异常事件信号进行特征提取，将特征矢量输入到一个基于三层BP神经网络而设计的分类器中对目标事件进行识别和分类.通过仿真目标信号和实际采样数据进行测试，对系统的报警识别率进行了验证，结果表明：对于仿真信号，系统的平均正确识别率达到了100%；对于实际采样数据，系统的平均正确识别率可以达到96.83%.

A fiber-optical perimeter intrusion detection system based on high-sensitivity FBG vibrating sensors with intelligent identification function was proposed. Stasitical features with an adaptive dynamic threshold were extracted in the time domain first. By inputting the extracted features into a three-layer Back-Propagation Artificial Neural Network classifier, the type of the event can be descerned and determined. The simulated signals and acquired data in the test field were both tested to validate the effectiveness of the proposed system and its identification method. The results show that the average identification rate of the system for the simulation signals can be up to 100%, and that for the actually acquired signals, the average identification rate of the system achieves 96.83%.