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Sensors 2015, 15(3), 4975-4995; doi:10.3390/s150304975

A Robust Kalman Framework with Resampling and Optimal Smoothing

Digital Sports Group, Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Haberstr. 2, 91058 Erlangen, Germany
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Academic Editor: Stefano Mariani
Received: 11 December 2014 / Revised: 29 January 2015 / Accepted: 11 February 2015 / Published: 27 February 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [9751 KB, uploaded 27 February 2015]   |  

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. View Full-Text
Keywords: Kalman filter; fixed-lag smoothing; outlier detection; real-time filtering; non-uniform sampling; parameter estimation Kalman filter; fixed-lag smoothing; outlier detection; real-time filtering; non-uniform sampling; parameter estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kautz, T.; Eskofier, B.M. A Robust Kalman Framework with Resampling and Optimal Smoothing. Sensors 2015, 15, 4975-4995.

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