A novel consensus approach to networked nonlinear filtering is introduced. The proposed approach is based on the idea of carrying out in parallel a consensus on likelihoods and a con- sensus on prior probability distributions and then combine the out- comes with a suitable weighting factor. Simulation experiments concerning a target tracking case-study show that the proposed consensus-based nonlinear filter can be convenient when only a few consensus iterations per sampling interval can be afforded.

Parallel consensus on likelihoods and priors for networked nonlinear filtering / G. Battistelli; L. Chisci; C. Fantacci. - In: IEEE SIGNAL PROCESSING LETTERS. - ISSN 1070-9908. - STAMPA. - 21:(2014), pp. 787-791. [10.1109/LSP.2014.2316258]

Parallel consensus on likelihoods and priors for networked nonlinear filtering

BATTISTELLI, GIORGIO;CHISCI, LUIGI;FANTACCI, CLAUDIO
2014

Abstract

A novel consensus approach to networked nonlinear filtering is introduced. The proposed approach is based on the idea of carrying out in parallel a consensus on likelihoods and a con- sensus on prior probability distributions and then combine the out- comes with a suitable weighting factor. Simulation experiments concerning a target tracking case-study show that the proposed consensus-based nonlinear filter can be convenient when only a few consensus iterations per sampling interval can be afforded.
2014
21
787
791
G. Battistelli; L. Chisci; C. Fantacci
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/911139
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