DocumentCode
68843
Title
Parallel Consensus on Likelihoods and Priors for Networked Nonlinear Filtering
Author
Battistelli, Giorgio ; Chisci, L. ; Fantacci, C.
Author_Institution
Dipt. di Ing. dell´Inf., Univ. di Firenze, Florence, Italy
Volume
21
Issue
7
fYear
2014
fDate
Jul-14
Firstpage
787
Lastpage
791
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 consensus on prior probability distributions and then combine the outcomes 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.
Keywords
iterative methods; nonlinear filters; probability; target tracking; consensus iteration; likelihood consensus; networked nonlinear filtering; parallel consensus approach; probability distribution; sampling interval; target tracking; weighting factor; Bayes methods; Filtering; Probability density function; Probability distribution; Signal processing algorithms; State estimation; Target tracking; Consensus; distributed state estimation; nonlinear filtering; sensor networks;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2014.2316258
Filename
6784395
Link To Document