基于时频混合特征提取算法的光纤传感信号识别研究

Time-frequency domain based hybrid feature extraction algorithm for fiber optic sensing events recognition

针对分布式光纤振动传感系统在实际应用中需对各个类别的传感事件进行快速准确的识别分类,提出了一种基于时频混合特征提取算法的识别分类方案。该方案采用具有时域特性的过零率和具有频域特性的小波包能量共同作为光纤传感事件的特征表述,而后利用基于径向基神经网络的分类器进行识别分类。经试验测试,该识别方案可以有效的从普通环境噪声中识别出光纤振动传感事件。其中,光纤振动传感事件的平均识别率为94.5%,识别响应时间小于0.3 s。

To accurately and rapidly recognize and classify different kinds of sensing events in distributed fiber optic vibration sensing system,a time-frequency based hybrid feature extraction algorithm has been proposed.In the algorithm,a zero crossing rate based time domain feature vector and a wavelet packet energy based frequency domain feature vector are used as the feature description of the given sensing event.Then,the feature vectors are classified by radial basis function neural network classifier.A series of experimental results show that the vibrations can be accurately recognized from the noise with high efficiency.Specifically,the average identification rate of 94.5%is achieved and the recognition response time can be limited in 0.3 s.