Denoising Algorithm for Φ-OTDR signals based on ICEEMDAN and wavelet thresholding

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 0.014 8, 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.