Multi-resolution autonomous linear state space filters for N-dimensional signals
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Autor:in (Körperschaft)
Publikationsdatum
2025
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
IEEE Transactions on Signal Processing
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
73
Ausgabe / Nummer
Seiten / Dauer
5303-5318
Patentnummer
Verlag / Herausgebende Institution
IEEE
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Linear filtering or convolution operations can be computationally expensive, especially in low-resource environ ments, high data rate applications, or scenarios involving large datasets. In particular, for multi-dimensional signals, the compu tational cost scales polynomially with the kernel size. In this paper, we propose the use of multi-dimensional Autonomous Linear State Space Models (ALSSMs) to reduce the computa tional complexity of convolution and correlation operations on N-dimensional signals and extend the method to (windowed) least-squares filters. For that, we work in an ALSSM subspace and provide efficient recursive computation rules that signifi cantly improve the efficiency of those filters. In addition, we provide various window functions and demonstrate paralleliza tion of computations and reuse of intermediate results for multi resolution analyses. Our method is particularly interesting for systems with limited resources, such as battery-powered and wearable devices, or when (real-time) processing of very large data sets is required, such as in the field of image processing and machine learning. We conclude with several practical examples, with ready-to-use implementations of the proposed methods as an open source Python repository.
Schlagwörter
Autonomous linear state space models, Convolution, Cross-correlation, Inner products, Linear filtering, Localized least squares, Localized windows, Multi-dimensional models, Multi-resolution filtering, N-dimensional signals, Recursive least squares, Subspaces
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1053-587X
1941-0476
1941-0476
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Hybrid
Lizenz
Zitation
Baeriswyl, C., Waldmann, F., Bertrand, A., & Wildhaber, R. (2025). Multi-resolution autonomous linear state space filters for N-dimensional signals. IEEE Transactions on Signal Processing, 73, 5303–5318. https://doi.org/10.1109/tsp.2025.3628349