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 / Vitetta, Giorgio M.; DI VIESTI, Pasquale; Sirignano, Emilio; Montorsi, Francesco. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - 68:1(2020), pp. 1002-1020. [10.1109/TSP.2020.2965296]
Multiple Bayesian Filtering as Message Passing
Giorgio M. Vitetta
;Pasquale Di Viesti;Emilio Sirignano;Francesco Montorsi
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.File | Dimensione | Formato | |
---|---|---|---|
tf_montorsi_final_BIO.pdf
Accesso riservato
Descrizione: bozza finale post-referaggio
Tipologia:
Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione
6.27 MB
Formato
Adobe PDF
|
6.27 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris