Multi-Channel Signal Denoising Method Based on Optimized MVMD Combined with Subspace Projection

To address the issue of noise interference in multichannel signals, which hinders the accurate extraction and transmission of valid information, this paper proposes a novel multichannel signal denoising algorithm. The algorithmleverages two key techniques: multivariate variational mode decomposition (MVMD) and subspace projection. First, we improve the MVMD using the Snake Herd Optimization (SO) algorithm, which is then applied to decompose the original signal into individual modal components. Subsequently, we compute the correlation coefficients between each modal component and the original signal, and select the components based on the magnitude of these coefficients. Finally, theselected modal components undergo further signal enhancement through the subspace projection method, and the modes are reconstructed to complete the denoising process. The effectiveness of the proposed method is verified through comparison with other methods. When applied to real-world localization problems, this method can provide higher localization accuracy.