Successive Jump and Mode Decomposition
Abstract
We propose fully data-driven variational methods, termed successive jump and mode decomposition (SJMD) and its multivariate extension, successive multivariate jump and mode decomposition (SMJMD), for successively decomposing nonstationary signals into amplitude- and frequency-modulated (AM-FM) oscillations and jump components. Unlike existing methods that treat oscillatory modes and jump discontinuities separately and often require prior knowledge of the number of components (K) -- which is difficult to obtain in practice -- our approaches employ successive optimization-based schemes that jointly handle AM-FM oscillations and jump discontinuities without the need to predefine K. Empirical evaluations on synthetic and real-world datasets demonstrate that the proposed algorithms offer superior accuracy and computational efficiency compared to state-of-the-art methods.
Cite
@article{arxiv.2504.08453,
title = {Successive Jump and Mode Decomposition},
author = {Mojtaba Nazari and Anders Rosendal Korshøj and Naveed ur Rehman},
journal= {arXiv preprint arXiv:2504.08453},
year = {2025}
}
Comments
Submitted to EUSIPCO 2025. arXiv admin note: text overlap with arXiv:2407.07800