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Convolution filter based methods for parameter estimation in general state--space models

Fabien Campillo 1, * Vivien Rossi 2 
* Corresponding author
1 MERE - Water Resource Modeling
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique : UMR0729
Abstract : The state-space modeling of partially observed dynamical systems generally requires estimates of unknown parameters. The dynamic state vector together with the static parameter vector can be considered as an augmented state vector. Classical filtering methods, such as the extended Kalman filter (EKF) and the bootstrap particle filter (PF), fail to estimate the augmented state vector. For these classical filters to handle the augmented state vector, a dynamic noise term should be artificially added to the parameter components or to the deterministic component of the dynamical system. However, this approach degrades the estimation performance of the filters. We propose a variant of the PF based on convolution kernel approximation techniques. This approach is tested on a simulated case study.
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https://hal.inria.fr/inria-00459886
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Submitted on : Thursday, February 25, 2010 - 2:40:45 PM
Last modification on : Friday, February 4, 2022 - 3:17:38 AM

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Fabien Campillo, Vivien Rossi. Convolution filter based methods for parameter estimation in general state--space models. IEEE Transactions on Aerospace and Electronic Systems, Institute of Electrical and Electronics Engineers, 2009, 45 (3), pp.1063-1071. ⟨10.1109/TAES.2009.5259183⟩. ⟨inria-00459886⟩

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