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Journal Articles IEEE Transactions on Aerospace and Electronic Systems Year : 2009

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

Vivien Rossi

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.

Dates and versions

inria-00459886 , version 1 (25-02-2010)

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Cite

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