A Robust Kalman Framework with Resampling and Optimal Smoothing

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Details zur Publikation

Autorinnen und Autoren: Kautz T, Eskofier B
Zeitschrift: Sensors
Verlag: MDPI
Jahr der Veröffentlichung: 2015
Band: 15
Heftnummer: 3
Seitenbereich: 4975 - 4995
ISSN: 1424-8220
Sprache: Englisch


The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate.

These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data.

The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.

FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Eskofier, Björn Prof. Dr.
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Kautz, Thomas
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)


Kautz, T., & Eskofier, B. (2015). A Robust Kalman Framework with Resampling and Optimal Smoothing. Sensors, 15(3), 4975 - 4995.

Kautz, Thomas, and Björn Eskofier. "A Robust Kalman Framework with Resampling and Optimal Smoothing." Sensors 15.3 (2015): 4975 - 4995.


Zuletzt aktualisiert 2018-10-08 um 06:54

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