Successive Jump and Mode Decomposition
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913803951669248 |
|---|---|
| author | Nazari, Mojtaba Korshøj, Anders Rosendal Rehman, Naveed ur |
| author_facet | Nazari, Mojtaba Korshøj, Anders Rosendal Rehman, Naveed ur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Successive Jump and Mode Decomposition Nazari, Mojtaba Korshøj, Anders Rosendal Rehman, Naveed ur Signal Processing 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. |
| title | Successive Jump and Mode Decomposition |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.08453 |