Collaborative Wi-Fi fingerprint training for indoor positioning

As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different...

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Main Authors:	Jing, Hao, Pinchin, James, Hill, Chris, Moore, Terry
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Online Access:	http://eprints.nottingham.ac.uk/33478/ http://eprints.nottingham.ac.uk/33478/ http://eprints.nottingham.ac.uk/33478/1/ION2014_HaoJing.pdf

id	nottingham-33478
recordtype	eprints
spelling	nottingham-334782018-07-02T09:05:38Z http://eprints.nottingham.ac.uk/33478/ Collaborative Wi-Fi fingerprint training for indoor positioning Jing, Hao Pinchin, James Hill, Chris Moore, Terry As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different situations and environment. Inertial sensor and Wi-Fi signal integrated indoor positioning have become good solutions to overcome many of the problems. Yet there are still problems such as inertial heading drift, wireless signal fluctuation and the time required for training a Wi-Fi fingerprint database. The collaborative Wi-Fi fingerprint training (cWiDB) method proposed in this paper enables the system to perform inertial measurement based collaborative positioning or Wi-Fi fingerprinting alternatively according to the current situation. It also reduces the time required for training the fingerprint database. Different database training methods and different training data size are compared to demonstrate the time and data required for generating a reasonable database. Finally the fingerprint positioning result is compared which indicates that the cWiDB is able to achieve the same positioning accuracy as conventional training methods but with less training time and a data adjustment option enabled. 2014-09-08 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.nottingham.ac.uk/33478/1/ION2014_HaoJing.pdf Jing, Hao and Pinchin, James and Hill, Chris and Moore, Terry (2014) Collaborative Wi-Fi fingerprint training for indoor positioning. In: 27th International Technical Meeting of The Satellite Division of the Institute of Navigation, 8-12 Sept 2014, Tampa, Florida. http://www.ion.org/publications/abstract.cfm?jp=p&articleID=12553
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
description	As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different situations and environment. Inertial sensor and Wi-Fi signal integrated indoor positioning have become good solutions to overcome many of the problems. Yet there are still problems such as inertial heading drift, wireless signal fluctuation and the time required for training a Wi-Fi fingerprint database. The collaborative Wi-Fi fingerprint training (cWiDB) method proposed in this paper enables the system to perform inertial measurement based collaborative positioning or Wi-Fi fingerprinting alternatively according to the current situation. It also reduces the time required for training the fingerprint database. Different database training methods and different training data size are compared to demonstrate the time and data required for generating a reasonable database. Finally the fingerprint positioning result is compared which indicates that the cWiDB is able to achieve the same positioning accuracy as conventional training methods but with less training time and a data adjustment option enabled.
format	Conference or Workshop Item
author	Jing, Hao Pinchin, James Hill, Chris Moore, Terry
spellingShingle	Jing, Hao Pinchin, James Hill, Chris Moore, Terry Collaborative Wi-Fi fingerprint training for indoor positioning
author_facet	Jing, Hao Pinchin, James Hill, Chris Moore, Terry
author_sort	Jing, Hao
title	Collaborative Wi-Fi fingerprint training for indoor positioning
title_short	Collaborative Wi-Fi fingerprint training for indoor positioning
title_full	Collaborative Wi-Fi fingerprint training for indoor positioning
title_fullStr	Collaborative Wi-Fi fingerprint training for indoor positioning
title_full_unstemmed	Collaborative Wi-Fi fingerprint training for indoor positioning
title_sort	collaborative wi-fi fingerprint training for indoor positioning
publishDate	2014
url	http://eprints.nottingham.ac.uk/33478/ http://eprints.nottingham.ac.uk/33478/ http://eprints.nottingham.ac.uk/33478/1/ION2014_HaoJing.pdf
first_indexed	2018-09-06T12:21:51Z
last_indexed	2018-09-06T12:21:51Z
_version_	1610860736168329216

Collaborative Wi-Fi fingerprint training for indoor positioning

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