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<identifier>oai:HAL:inria-00459886v1</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Convolution filter based methods for parameter estimation in general state--space models</title>
<creator>Campillo, Fabien</creator>
<creator>Rossi, Vivien</creator>
<contributor>Water Resource Modeling (MERE) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>ISSN: 0018-9251</source>
<source>IEEE Transactions on Aerospace and Electronic Systems</source>
<publisher>Institute of Electrical and Electronics Engineers</publisher>
<identifier>inria-00459886</identifier>
<identifier>https://hal.inria.fr/inria-00459886</identifier>
<source>https://hal.inria.fr/inria-00459886</source>
<source>IEEE Transactions on Aerospace and Electronic Systems, Institute of Electrical and Electronics Engineers, 2009, 45 (3), pp.1063-1071. 〈10.1109/TAES.2009.5259183〉</source>
<identifier>DOI : 10.1109/TAES.2009.5259183</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/TAES.2009.5259183</relation>
<language>en</language>
<subject lang=it>mere2009</subject>
<subject lang=it>state-space models</subject>
<subject lang=it>parameter estimation</subject>
<subject lang=it>bootstrap particle filter</subject>
<subject lang=it>convolution kernel</subject>
<subject lang=it>convolution particle filter</subject>
<subject>[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>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.</description>
<date>2009</date>
</dc>
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