Gaussian process model enabled particle filter for device-free localization

Song, B; Wang, H; Xiao, W; Huang, S; Shi, L

Gaussian process model enabled particle filter for device-free localization

Song, B Wang, H Xiao, W Huang, S

Shi, L

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Publication Type:: Conference Proceeding
Citation:: 20th International Conference on Information Fusion, Fusion 2017 - Proceedings, 2017
Issue Date:: 2017-08-11

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Field	Value	Language
dc.contributor.author	Song, B	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Xiao, W	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.date.issued	2017-08-11	en_US
dc.identifier.citation	20th International Conference on Information Fusion, Fusion 2017 - Proceedings, 2017	en_US
dc.identifier.isbn	9780996452700	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119026
dc.description.abstract	© 2017 International Society of Information Fusion (ISIF). Device-free localization (DFL) is an emerging wireless network target localization technique that does not need to attach any electronic device with the target. It is remaining as a challenging research problem due to the weak wireless signals and the uncertain wireless communication environment. In this paper, a novel Gaussian Process (GP) based wireless propagation model is proposed to describe the likelihood relationship between the target location and the changes of the RSS measurement for a wireless link. Sequentially Particle Filter (PF) is applied to the DFL for estimating the location of the target, after the GP model is trained using the experimental measurements of the link. Experimental results demonstrate that the proposed GP-PF algorithm can track the target with much better localization accuracy than the Support Vector Machine (SVM) based PF approach.	en_US
dc.relation.ispartof	20th International Conference on Information Fusion, Fusion 2017 - Proceedings	en_US
dc.relation.isbasedon	10.23919/ICIF.2017.8009778	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Gaussian process model enabled particle filter for device-free localization	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 International Society of Information Fusion (ISIF). Device-free localization (DFL) is an emerging wireless network target localization technique that does not need to attach any electronic device with the target. It is remaining as a challenging research problem due to the weak wireless signals and the uncertain wireless communication environment. In this paper, a novel Gaussian Process (GP) based wireless propagation model is proposed to describe the likelihood relationship between the target location and the changes of the RSS measurement for a wireless link. Sequentially Particle Filter (PF) is applied to the DFL for estimating the location of the target, after the GP model is trained using the experimental measurements of the link. Experimental results demonstrate that the proposed GP-PF algorithm can track the target with much better localization accuracy than the Support Vector Machine (SVM) based PF approach.

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http://hdl.handle.net/10453/119026