Distributed Monte Carlo information fusion and distributed particle filtering

Manuel, IL; Bishop, AN

Distributed Monte Carlo information fusion and distributed particle filtering

Manuel, IL Bishop, AN

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Publication Type:: Conference Proceeding
Citation:: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 8681 - 8688
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Manuel, IL	en_US
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 8681 - 8688	en_US
dc.identifier.isbn	9783902823625	en_US
dc.identifier.issn	1474-6670	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33034
dc.description.abstract	© IFAC. We present a Monte Carlo solution to the distributed data fusion problem and apply it to distributed particle filtering. The consensus-based fusion algorithm is iterative and it involves the exchange and fusion of empirical posterior densities between neighbouring agents. As the fusion method is Monte Carlo based it is naturally applicable to distributed particle filtering. Furthermore, the fusion method is applicable to a large class of networks including networks with cycles and dynamic topologies. We demonstrate both distributed fusion and distributed particle filtering by simulating the algorithms on randomly generated graphs.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120102873
dc.relation.ispartof	IFAC Proceedings Volumes (IFAC-PapersOnline)	en_US
dc.title	Distributed Monte Carlo information fusion and distributed particle filtering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	19	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Cape Town, South Africa
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© IFAC. We present a Monte Carlo solution to the distributed data fusion problem and apply it to distributed particle filtering. The consensus-based fusion algorithm is iterative and it involves the exchange and fusion of empirical posterior densities between neighbouring agents. As the fusion method is Monte Carlo based it is naturally applicable to distributed particle filtering. Furthermore, the fusion method is applicable to a large class of networks including networks with cycles and dynamic topologies. We demonstrate both distributed fusion and distributed particle filtering by simulating the algorithms on randomly generated graphs.

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http://hdl.handle.net/10453/33034