Adaptive Particle Filter for Stable Distribution

H. F. de Campos Velho () and H. C. Morais Furtado ()
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H. F. de Campos Velho: Instituto Nacional de Pesquisas Espaciais (INPE)
H. C. Morais Furtado: Instituto Nacional de Pesquisas Espaciais (INPE)

A chapter in Integral Methods in Science and Engineering, 2011, pp 47-57 from Springer

Abstract: Abstract Particle filters are considered as the most robust method in the estimation theory. However, all techniques presented in the literature consider only cases where the data can be described by a probability distribution function (PDF) with all statistical moments well defined. The present chapter a novel particle filter is introduced for estimating a posteriori PDF using a Bayesian scheme. The key issue is to consider an adaptive likelihood function. The scheme generalizes the traditonal particle filter approaches. The scheme can be applied to inverse problem, control theory, image and signal processing, and data assimilation.

Keywords: Inverse Problem; Probability Density Function; Central Limit Theorem; Data Assimilation; Probability Density Function (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-8176-8238-5_6

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DOI: 10.1007/978-0-8176-8238-5_6

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