Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

To eliminate the noise from signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized hybrid of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), correlation coefficient (CC) judgment to perform signal denoising named as MVO-PSO-VMD-CC-WT, whose fitness function root mean square error (RMSE) individual parameters VMD. For every individual, original decomposed VMD into pure components, noisy components terms CC judgment, where are directly retained, denoised WT discarded, then, reconstructed be signal. Then, obtained optimal utilized MVO-PSO-VMD-CC-WT use above repeated processing. Two simulated experimental results show that which has highest signal-to-noise ratio least RMSE superior other compared algorithms. And effectively applied actual lake experiments. Therefore, suitable for can experiments