Geometry and motion-based positioning algorithms for mobile tracking in NLOS environments

Yu, K; Dutkiewicz, E

Geometry and motion-based positioning algorithms for mobile tracking in NLOS environments

Yu, K Dutkiewicz, E

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Mobile Computing, 2012, 11 (2), pp. 254 - 263
Issue Date:: 2012-02-01

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Field	Value	Language
dc.contributor.author	Yu, K	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.date.issued	2012-02-01	en_US
dc.identifier.citation	IEEE Transactions on Mobile Computing, 2012, 11 (2), pp. 254 - 263	en_US
dc.identifier.issn	1536-1233	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114962
dc.description.abstract	This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Mobile Computing	en_US
dc.relation.isbasedon	10.1109/TMC.2011.24	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Geometry and motion-based positioning algorithms for mobile tracking in NLOS environments	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	11	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	100503 Computer Communications Networks	en_US
utslib.for	1005 Communications Technologies	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms. © 2012 IEEE.

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http://hdl.handle.net/10453/114962