提出了一种基于局部均值分解(LMD)和串行特征融合(SFF)的光纤周界振动信号识别方法。该方法先去除噪声,提取振动信号的相关信息,再进行SFF以得到具有准确描述能力的特征向量,最后采用概率神经网络(PNN)算法进行学习和分类。利用不同单一振动信号和风雨天气干扰下的不同振动信号对该方法进行验证。结果表明,该方法在上述两种情况下的平均正确识别率分别达到96.0%和96.7%,识别时间分别为0.87 s和0.91 s,在敏感信息识别和特征提取方面明显优于传统的LMD算法和SFF-PNN算法。

A method for the recognition of fiber-optic perimeter vibration signals is proposed based on local mean decomposition (LMD) and serial feature fusion (SFF), in which the effect of noise is first suppressed to extract the relevant information of vibration signals, then the SFF is conducted to get the feature vectors with the ability of accurate description, and finally the probabilistic neural network (PNN) algorithm is adopted for learning and classification. The proposed method is validated by different single-vibration signals and vibration signals under the stormy weather interference. The results show that, by the proposed method in the above two cases, the average correct-recognition rates reach 96.0% and 96.7%, and the recognition time is 0.87 s and 0.91 s, respectively. The proposed method is superior to the traditional LMD algorithm and the SFF-PNN algorithm in the sensitive information recognition and feature extraction.