Successive Multichannle Variational Mode Decomposition

Abstract

• Variational Mode Decomposition (VMD) has been being a powerful tool for concurrently decomposing a signal into a discrete number of sub-signals (modes). However, the existing VMD method and its multivariate extension, a.k.a. multivariate VMD (MVMD), require the prior knowledge of the number of modes for elegant performance. Besides, the joint optimization over all the modes calls for heavy computational complexity, thus limiting their practical applications.

• We developed a Successive Multichannel Variational Mode Decomposition (SMVMD) method to successively extract the signal modes without any prior information about the mode number.
• In a successive manner, we simplify the original high-dimensional optimization model into multiple low-dimensional optimization problems, which significantly reduces the computation complexity.

The work is underview and will come soon…

Key words:Mode Decomposition, Successive Decomposition, Multichannel, Joint Optimization.