Abstract
Signal decomposition techniques aim to break down nonstationary signals
into their oscillatory components, serving as a preliminary step in
various practical signal processing applications. This has motivated
researchers to explore different strategies, yielding several distinct
approaches. A wellknown optimization-based method, the Variational Mode
Decomposition (VMD), relies on the formulation of an optimization
problem, utilizing constant bandwidth Wiener filters. However, this
poses limitations in constant bandwidth and the need for constituent
count. In this paper, a new method, namely Dynamic Bandwidth VMD
(DB-VMD), is proposed to generalize VMD by addressing the Wiener filter
limitations through enhancement of the optimization problem with an
additional constraint. Experiments in synthetic signals highlight
DB-VMDâ\euro™s noise robustness and adaptability in comparison to VMD,
paving the way for many applications, especially when the analyzed
signals are contaminated with noise.