基于经验模态分解与巴特沃斯滤波的Φ-OTDR去噪算法

Φ-OTDR Denoising Algorithm Based on Empirical Mode Decomposition and Butterworth Filtering

针对Φ-OTDR光纤传感器采集到的原始信号中含有大量噪声、数据信噪比低的问题,提出一种基于经验模态分解和区间迭代不变阈值的光纤传感信号噪声去除方法。该方法从信号的时间尺度特征出发进行信号分解,无需预先设定基函数,同时采用标准的巴特沃斯滤波器对本征模态分量进行滤波处理。设计仿真实验进行测试,结果表明,该方法对人工敲击信号和机械振动信号的信噪比提升分别为3.01 dB和5.12 dB,能有效抑制原始信号数据的噪声,从而提高Φ-OTDR系统的灵敏度。

In order to solve the problem that the original signal collected byΦOTDR optical fiber sensor contains a lot of noise and low signaltonoise ratio,a denoising algorithm based on empirical mode decomposition and interval iterative invariant threshold is proposed.In this method,the signal is decomposed based on the time scale features,without setting the basis function in advance.And then a standard Butterworth filter is used to eliminate the noise in the intrinsic model components.Simulation experiments are designed,and the results show that the signaltonoise ratio of humman beating signal and mechanical excavation signal is improved by 3.01 dB and 5.12 dB respectively,which can effectively restrain the noise of original signal data,thus improving the sensitivity ofΦOTDR system.