针对光纤周界预警系统输出信号的非平稳特性，提出了一种基于总体平均经验模态分解(EEMD)的模式识别方法。预警系统基于Mach-Zehnder 干涉原理，利用4 条单模光纤构成分布式扰动传感器，实时监测周界入侵事件。该方法引用具有自适应性的EEMD 算法将振动信号分解成多个本征模态函数(IMF)。根据不同振动信号能量各异的特点，提出EEMD 能量熵的方法排除非入侵的干扰。最后建立双重支持向量机对入侵信号进行识别。实验结果表明：该方法可以有效排除非人为入侵的干扰，准确识别攀爬、敲击和其他虚警信号，平均正确识别率优于92%，提高了系统的报警识别率，降低了误报率。

A pattern recognition method based on ensemble empirical mode decomposition (EEMD) is proposed for the non-stationary features of output signal in the fiber-optic intrusion monitoring system. The system based on the principle of Mach-Zehnder interferometer and four single-mode optical fibers in the cable are utilized to build up the distributed crosstalk sensor, by which the real-time detection of abnormal events can be realized. The vibration signals are decomposed into a series of intrinsic mode functions (IMF) using the EEMD algorithm with self-adaptability. According to the characteristics of the various vibration signal intensities, a method using the EEMD energy entropy to eliminate the disturbance of non-intrusion events is proposed. Double support vector machine is built to identify the intrusion type. The experimental results illustrate that this method can evidently get rid of the non-intrusion disturbance and effectively discern different intrusion events such as fence-climbing, cableknocking and other signals. The correct recognition rate in average is greater than 92%. What′s more, the alarm rate is increased and the false alarm rate is reduced in the system.