Outlier Rejection Methods for Robust Kalman Filtering

Kim, D, Lee, S and Jeon, M 2011, 'Outlier Rejection Methods for Robust Kalman Filtering' in James J. Park, Laurence T. Yang & Changhoon Lee (ed.) Future Information Technology: 6th International Conference, FutureTech 2011, Loutraki, Greece, June 28-30, 2011, Proceedings, Part I, Springer Science & Business Media, Berlin, Germany, pp. 316-322.


Document type: Book Chapter
Collection: Book Chapters

Title Outlier Rejection Methods for Robust Kalman Filtering
Author(s) Kim, D
Lee, S
Jeon, M
Year 2011
Title of book Future Information Technology: 6th International Conference, FutureTech 2011, Loutraki, Greece, June 28-30, 2011, Proceedings, Part I
Publisher Springer Science & Business Media
Place of publication Berlin, Germany
Editor(s) James J. Park, Laurence T. Yang & Changhoon Lee
Start page 316
End page 322
Subjects Signal Processing
Summary In this paper we discuss efficient methods of the state estimation which are robust against unknown outlier measurements. Unlike existing Kalman filters, we relax the Gaussian assumption of noises to allow sparse outliers. By doing so spikes in channels, sensor failures, or intentional jamming can be effectively avoided in practical applications. Two approaches are suggested: median absolute deviation (MAD) and L1-norm regularized least squares (L1-LS). Through a numerical example two methods are tested and compared.
Copyright notice © Springer-Verlag Berlin Heidelberg 2011
Keyword(s) kalman filtering
median absolute deviation
L1-norm optimization.
DOI - identifier 10.1007/978-3-642-22333-4
ISBN 9783642223327
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