Indoor positioning using BLE iBeacon, smartphone sensors, and distance-based position correction algorithm

Trinh, AVT; Dinh, TMT; Nguyen, QT; Sandrasegaran, K

Indoor positioning using BLE iBeacon, smartphone sensors, and distance-based position correction algorithm

Trinh, AVT Dinh, TMT Nguyen, QT Sandrasegaran, K

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Intelligent Computing in Engineering, 2020, 1125, pp. 1007-1016
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Trinh, AVT
dc.contributor.author	Dinh, TMT
dc.contributor.author	Nguyen, QT
dc.contributor.author	Sandrasegaran, K https://orcid.org/0000-0003-1270-5756
dc.date	2019-08-08
dc.date.accessioned	2021-05-10T04:23:22Z
dc.date.available	2021-05-10T04:23:22Z
dc.date.issued	2020-01-01
dc.identifier.citation	Intelligent Computing in Engineering, 2020, 1125, pp. 1007-1016
dc.identifier.isbn	9789811527791
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/148812
dc.description.abstract	In this paper, we propose a Bluetooth Low Energy (BLE) iBeacon-based localization system, in which we combine two popular positioning methods: Pedestrian Dead Reckoning (PDR) and fingerprinting. As we build the system as an application running on an iPhone, we choose Kalman filter as the fusion algorithm to avoid complex computation. In fingerprinting, a multi-direction-database approach is applied. Finally, in order to reduce the cumulative error of PDR due to smartphone sensors, we propose an algorithm called “Distance-based Position Correction”. The aim of this algorithm is to occasionally correct the estimated position by using the iBeacon nearest to the user. In experiments, our system results in an average error of only 0.63 m.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Intelligent Computing in Engineering
dc.relation.ispartof	International Conference on Research in Intelligent and Computing in Engineering
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing
dc.relation.isbasedon	10.1007/978-981-15-2780-7_105
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Indoor positioning using BLE iBeacon, smartphone sensors, and distance-based position correction algorithm
dc.type	Conference Proceeding
utslib.citation.volume	1125
utslib.location.activity	Vietnam
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 - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-10T04:23:21Z
pubs.finish-date	2019-08-09
pubs.place-of-publication	Singapore
pubs.publication-status	Published
pubs.start-date	2019-08-08
pubs.volume	1125
dc.location	Singapore

Abstract:

In this paper, we propose a Bluetooth Low Energy (BLE) iBeacon-based localization system, in which we combine two popular positioning methods: Pedestrian Dead Reckoning (PDR) and fingerprinting. As we build the system as an application running on an iPhone, we choose Kalman filter as the fusion algorithm to avoid complex computation. In fingerprinting, a multi-direction-database approach is applied. Finally, in order to reduce the cumulative error of PDR due to smartphone sensors, we propose an algorithm called “Distance-based Position Correction”. The aim of this algorithm is to occasionally correct the estimated position by using the iBeacon nearest to the user. In experiments, our system results in an average error of only 0.63 m.

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http://hdl.handle.net/10453/148812