Abstract
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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Kim, D.Y., Lee, SG., Jeon, M. (2011). Outlier Rejection Methods for Robust Kalman Filtering. In: Park, J.J., Yang, L.T., Lee, C. (eds) Future Information Technology. Communications in Computer and Information Science, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22333-4_41
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DOI: https://doi.org/10.1007/978-3-642-22333-4_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22332-7
Online ISBN: 978-3-642-22333-4
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