MGDMD: Multi-variate generalized dispersive mode decomposition

Abstract

In this paper, we propose a multi-variate extension of the recently proposed generalized dispersive mode decomposition (GDMD) which can accurately estimate cross group delays and extract overlapped dispersive modes. We propose a joint dispersion optimization solution for multi-channel dispersive signals which can extract all high-quality overlapped dispersive modes with very few cross dispersion effects. Simulation results on multi-channel synthetic dispersive signals and real-life signals taken from four publicly available datasets depict the superiority of the proposed multi-variate GDMD (MGDMD) as compared to existing decomposition techniques.