Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise and Its Application in Noise Reduction for Fiber Optic Sensing

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) addresses the incomplete decomposition problem in ensemble empirical mode decomposition (EEMD) during the ensemble averaging process, but the residual noise in intrinsic mode function (IMF) severely affect the decomposition results and the efficiency of the algorithm. To address the shortcomings of CEEMDAN, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm is presented. This method introduces pairs of Gaussian white noise with opposite amplitudes to assist in the decomposition of original signal. The final IMF is the ensemble of both the IMFs with positive and negative noises, where the introduced noise can be effectively neutralized. Simulation and experimental results indicate that ICEEMDAN has advantages in improving the efficiency and decomposition accuracy of the algorithm. The proposed approach was applied to signal denoising in fiber optic sensing, and the analysis results verified the effectiveness and superiority of the method.