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Marginalized Adaptive Particle Filtering for Nonlinear Models with Unknown Time-Varying Noise Parameters
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Institute of Information Theory and Automation, Czech Republic.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 6, 1566-1575 p.Article in journal (Refereed) Published
Abstract [en]

Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear Gaussian models, Adaptive Kalman filters (AKF) estimate unknown parameters in the noise distributions jointly with the state. For nonlinear models, we provide a Bayesian solution for the estimation of the noise distributions in the exponential family, leading to a marginalized adaptive particle filter (MAPF) where the noise parameters are updated using finite dimensional sufficient statistics for each particle. The time evolution model for the noise parameters is defined implicitly as a Kullback-Leibler norm constraint on the time variability, leading to an exponential forgetting mechanism operating on the sufficient statistics. Many existing methods are based on the standard approach of augmenting the state with the unknown variables and attempting to solve the resulting filtering problem. The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state. Further, the MAPF can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets. We illustrate the MAPF on first a standard example, and then on a tire radius estimation problem on real data.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 49, no 6, 1566-1575 p.
Keyword [en]
Unknown noise statistics, Adaptive filtering, Marginalized particle filter, Bayesian conjugate prior
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-94600DOI: 10.1016/j.automatica.2013.02.046ISI: 000319540500005OAI: oai:DiVA.org:liu-94600DiVA: diva2:633613
Funder
Swedish Research Council
Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2017-12-06

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Özkan, EmreSaha, SaikatLundquist, ChristianGustafsson, Fredrik

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  • apa
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