A Robust Kalman Framework with Resampling and Optimal Smoothing

Journal article
(Original article)


Publication Details

Author(s): Kautz T, Eskofier B
Journal: Sensors
Publisher: MDPI
Publication year: 2015
Volume: 15
Journal issue: 3
Pages range: 4975 - 4995
ISSN: 1424-8220
Language: English


Abstract


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 Authors / FAU Editors

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


How to cite

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

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

BibTeX: 

Last updated on 2018-19-04 at 03:30