Bias aware Kalman filters: Comparison and improvements

J.-P. Drecourt, H. Madsen, Dan Rosbjerg

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter:
    the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without
    feedback of the bias into the model state. The colored noise filter formulation is extended to correct both time correlated and uncorrelated
    model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented
    in an ensemble framework using Latin hypercube sampling. The techniques are illustrated on a simple one-dimensional
    groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations
    with bias feedback work in more general conditions than the implementations without feedback.
    2005 Elsevier Ltd. All rights reserved.
    Original languageEnglish
    JournalAdvances in Water Resources
    Volume29
    Pages (from-to)707–718
    DOIs
    Publication statusPublished - 2006

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