In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.

Multiple Bayesian Filtering as Message Passing

Viesti, Pasquale Di;
2020

Abstract

In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.
Vitetta, Giorgio M.; Viesti, Pasquale Di; Sirignano, Emilio; Montorsi, Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/733518
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