Multi-directional weighted interpolation for wi-fi localisation

Bowie, D; Faichney, J; Blumenstein, M

Multi-directional weighted interpolation for wi-fi localisation

Bowie, D Faichney, J Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 274 pp. 105 - 112
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Bowie, D	en_US
dc.contributor.author	Faichney, J	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 274 pp. 105 - 112	en_US
dc.identifier.isbn	9783319055817	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121098
dc.description.abstract	© Springer International Publishing Switzerland 2014. The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limitations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi localisation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final precision, and the time and money spent training the system. We propose Multi-Directional Weighted Interpolation (MDWI), a probabilistic-based weighting mechanism to predict unseen locations. Our results show that MDWI uses half the number of training points whilst increasing accuracy by up to 24%.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-319-05582-4_9	en_US
dc.title	Multi-directional weighted interpolation for wi-fi localisation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	274	en_US
utslib.for	0803 Computer Software	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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	274	en_US

Abstract:

© Springer International Publishing Switzerland 2014. The rise in popularity of unmanned autonomous vehicles (UAV) has created a need for accurate positioning systems. Due to the indoor limitations of the Global Positioning System (GPS), research has focused on other technologies which could be used in this landscape with Wi-Fi localisation emerging as a popular option. When implementing such a system, it is necessary to find an equilibrium between the desired level of final precision, and the time and money spent training the system. We propose Multi-Directional Weighted Interpolation (MDWI), a probabilistic-based weighting mechanism to predict unseen locations. Our results show that MDWI uses half the number of training points whilst increasing accuracy by up to 24%.

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http://hdl.handle.net/10453/121098