Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Abstract

Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

“…The algorithm successfully removes baseline drift and ocean noise and achieves high-precision orientation of the target during DOA estimation. It outperforms the literature (Hu et al , 2019; Jiang et al , 2024), is consistent with actual measurement conditions and the proposal of this joint algorithm is of great research significance for accurately locating underwater targets.…”

“…From the figures, it's evident that these three methods can eliminate existing noise components while maintaining local waveform characteristics and peak values, thus restoring the signal with a certain accuracy. Comparatively, the signal reconstructed by the CEEMDAN method retains more complete and smoother effective signal components, superior to the literature (Jiang et al, 2024); it is able to fully recover the waveform in the time domain, followed by the EEMD method, whereas the signal reconstructed by the EMD method contains some sharp peaks.…”

“…Sample entropy serves as a quantitative measure of signal complexity, enabling us to assess its characteristics based on the decomposed intrinsic modal components and the signal's sample entropy. A smaller sample entropy indicates lower time series complexity, higher self-similarity and The improvement over reference (Hu et al, 2019;Jiang et al, 2024) lies in the easier selection of effective intrinsic mode functions, thereby better distinguishing signals from noise.…”

“…A smaller sample entropy indicates lower time series complexity, higher self-similarity and greater correlation with the signal; conversely, a larger sample entropy suggests lower time series autocorrelation. The improvement over reference (Hu et al , 2019; Jiang et al , 2024) lies in the easier selection of effective intrinsic mode functions, thereby better distinguishing signals from noise.…”