光纤入侵信号的特征提取与识别算法

Feature Extraction and Recognition Algorithm for Fiber Intrusion Signals

为了对分布式光纤上的入侵信号类型进行准确识别,提出了一种基于集合经验模态分解(EEMD)结合随机向量函数链接(RVFL)神经网络的光纤入侵信号的特征提取与识别算法。算法步骤为:对采集到的光纤入侵信号作预处理操作,包括最小-最大规范化处理和利用db3小波去除信号的低频噪声;采用EEMD方法对入侵信号进行分解,得到5组本征模态函数(IMF);计算各IMF分量的能量占比,并依据方差分析法筛选出3组特征向量;将特征向量送入RVFL神经网络进行训练并对入侵信号进行识别。实验结果显示:该方法能正确识别不同入侵信号的类型,具有较高的准确率。

A feature extraction and recognition algorithm for fiber intrusion signals is proposed based on ensemble empirical-mode decomposition(EEMD)coupled with a random vector-function linked(RVFL)neural network to accurately identify the type of intrusion signal on a distributed optical fiber.The proposed algorithm starts with the preprocessing for the collected fiber intrusion signals,including minimum-maximum normalization processing and the removal of low frequency noise using the db3 wavelet.Then,the intrusion signals are decomposed by the EEMD to obtain five groups of intrinsic mode functions(IMF).Subsequently,the energy ratio of each component of the IMF is calculated,and three feature vectors are filtered using the analysis of variance.Finally,the feature vectors are sent into the RVFL neural network to be trained for the completion of the signal recognition.The experimental results validate that the proposed algorithm can accurately distinguish between different intrusion signals with high recognition rate.