Fusion of spatially referring natural language statements with random set theoretic likelihoods

Bishop, AN; Ristic, B

Fusion of spatially referring natural language statements with random set theoretic likelihoods

Bishop, AN

Ristic, B

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Aerospace and Electronic Systems, 2013, 49 (2), pp. 932 - 944
Issue Date:: 2013-04-17

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Field	Value	Language
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.contributor.author	Ristic, B	en_US
dc.date.issued	2013-04-17	en_US
dc.identifier.citation	IEEE Transactions on Aerospace and Electronic Systems, 2013, 49 (2), pp. 932 - 944	en_US
dc.identifier.issn	0018-9251	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116198
dc.description.abstract	Localisation via the fusion of spatially referring natural language statements is considered here. The contribution lies in the underlying problem formulation and a robust modelling framework. Random-set-based estimation is the underlying mathematical formalism. Each statement generates a generalised likelihood function. A Bayesian filter is outlined that takes a sequence of likelihoods generated by multiple statements. The idea is to recursively build a map over the state space that can be used to infer the state. © 1965-2011 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120102873
dc.relation.ispartof	IEEE Transactions on Aerospace and Electronic Systems	en_US
dc.relation.isbasedon	10.1109/TAES.2013.6494390	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Aerospace & Aeronautics	en_US
dc.title	Fusion of spatially referring natural language statements with random set theoretic likelihoods	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	49	en_US
utslib.for	0901 Aerospace Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

Localisation via the fusion of spatially referring natural language statements is considered here. The contribution lies in the underlying problem formulation and a robust modelling framework. Random-set-based estimation is the underlying mathematical formalism. Each statement generates a generalised likelihood function. A Bayesian filter is outlined that takes a sequence of likelihoods generated by multiple statements. The idea is to recursively build a map over the state space that can be used to infer the state. © 1965-2011 IEEE.

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http://hdl.handle.net/10453/116198