基于傅里叶分解与排列熵降噪方法的光纤入侵信号分类

Fiber Intrusion Signal Classification Based on Fourier Decomposition and Permutation Entropy Noise Reduction Method

为了准确识别分布式光纤预警系统中的入侵信号类型,提出了一种基于傅里叶分解方法(FDM)与排列熵降噪方法的光纤入侵信号特征提取与识别算法。首先,用FDM将光纤入侵信号分解为若干个固有频带函数(FIBF)。然后,计算各FIBF分量的排列熵,利用排列熵对噪声的敏感特性筛选出符合条件的FIBF并重构信号。最后,计算重构信号的近似熵与能量并构造二维特征向量,将其送入支持向量机进行训练后识别光纤入侵信号。实验结果表明,该算法可以有效识别敲击、小跑、过车三类光纤入侵信号,平均识别准确率为93.33%。

In order to accurately identify the types of intrusion signals in the distributed optical fiber early warning system,this paper proposes an optical fiber intrusion signal feature extraction and recognition algorithm based on Fourier decomposition method(FDM)and permutation entropy noise reduction method.First,the FDM is used to decompose the fiber intrusion signal into a number of intrinsic frequency band functions(FIBF).Then,the permutation entropy of each FIBF component is calculated,the sensitivity of permutation entropy to noise is used to screen qualified FIBF,and the signal is reconstructed.Finally,the approximate entropy and energy of the reconstructed signal are calculated and a twodimensional feature vector is constructed,which is sent to the support vector machine for training and recognizes the fiber intrusion signal.Experimental results show that the algorithm can effectively identify three types of optical fiber intrusion signals:tapping,trot and passing,with an average recognition accuracy of 93.33%.