基于ICEEMDAN和小波阈值的Φ-OTDR信号去噪算法

Denoising Algorithm forΦ-OTDR signals based on ICEEMDAN and wavelet thresholding

针对分布式光纤声波传感系统信号信噪比较低的问题,提出了一种改进的自适应噪声完全集成经验模态分解方法。改进方法利用样本熵和小波阈值去噪算法,从高噪声分量中提取有效成分。通过改进的自适应噪声完全集成经验模态分解(ICEEMDAN,Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise)对实际采集的信号进行分解,计算样本熵,将其中的含噪分量进行小波阈值去噪,最后与未处理的信号分量进行重构。实验结果表明,对实采的信号进行降噪处理后,信噪比提高了5.34 dB,均方误差降低了0.0148,波形互相关系数提高了5.7%。与其他常用的去噪方法相比,该方法不仅在信噪比方面表现更优秀,而且在均方误差和波形互相关系数方面也具有更好的性能,能够更好地保留有用信号。

This paper proposes an improved adaptive noise-aided complete ensemble empirical mode decomposition(ICEEMDAN)method to address the issue of low signal-to-noise ratio in distributed optical fiber acoustic sensing systems.The proposed approach utilizes sample entropy and wavelet threshold denoising algorithm to extract valuable components from high noise components.The ICEEMDAN is applied to decompose the acquired signals,and sample entropy is calculated to identify the noisy components,which are then subjected to wavelet threshold denoising.Finally,the denoised components are reconstructed with the untreated intrinsic mode functions.Experimental results demonstrate that the denoising treatment significantly enhances the signal-to-noise ratio by 5.34 dB,reduces the mean square error by 0.0148,and improves waveform similarity by 5.7%.Compared to other commonly used denoising methods,the proposed approach not only exhibits superior performance in terms of signal-to-noise ratio but also demonstrates better performance in mean square error and waveform similarity,thereby preserving useful signals more effectively.