Multi-observers of particle filter approach for estimating indoor mobile user location

Location-aware personal computing application can work accurately by estimating user location using IEEE 802.11 (Wi-Fi) signals in indoor environment. Nowadays, Wi-Fi is more and more widely available and installed on most mobile devices. Unfortunately, the Wi-Fi’s signal fluctuates greatly up to 33...

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Main Authors:	Mantoro, Teddy, Ayu, Media Anugerah, Usino, Wendi, Raman, Shakiratul Husna, Md Latiff, Nurul Hidayati
Format:	Conference or Workshop Item
Language:	English English
Published:	2011
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/1/PID2043681_ParticleFilter_Mantoro.pdf http://irep.iium.edu.my/6971/4/Multi-observers_of_Particle_Filter_Approach.pdf

id	iium-6971
recordtype	eprints
spelling	iium-69712012-04-24T08:25:04Z http://irep.iium.edu.my/6971/ Multi-observers of particle filter approach for estimating indoor mobile user location Mantoro, Teddy Ayu, Media Anugerah Usino, Wendi Raman, Shakiratul Husna Md Latiff, Nurul Hidayati T Technology (General) Location-aware personal computing application can work accurately by estimating user location using IEEE 802.11 (Wi-Fi) signals in indoor environment. Nowadays, Wi-Fi is more and more widely available and installed on most mobile devices. Unfortunately, the Wi-Fi’s signal fluctuates greatly up to 33%, some of the causes are reflection, refraction, temperature, humidity, the dynamic environment. These make user location not in good accuracy to be estimated. In this paper we propose the use of Particle Filter to improve user location estimation which involves the modeling of non-linear and non-Gaussian systems. To make the estimation accurate, the real time data of multi-observer Wi-Fi signals is used. The loss of diversity and parameter chosen in order to reduce the ambiguity is observed in tracking user location. We improve the Particle Filter Algorithm by giving target/reference points and by reducing the computational complexity. This paper shows that the estimation of the location from real-time data is close to the real tracking object. 2011-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/6971/1/PID2043681_ParticleFilter_Mantoro.pdf application/pdf en http://irep.iium.edu.my/6971/4/Multi-observers_of_Particle_Filter_Approach.pdf Mantoro, Teddy and Ayu, Media Anugerah and Usino, Wendi and Raman, Shakiratul Husna and Md Latiff, Nurul Hidayati (2011) Multi-observers of particle filter approach for estimating indoor mobile user location. In: TENCON 2011, 21-24 November 2011, Bali . http://tencon2011.com/home/show/id/2
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Mantoro, Teddy Ayu, Media Anugerah Usino, Wendi Raman, Shakiratul Husna Md Latiff, Nurul Hidayati Multi-observers of particle filter approach for estimating indoor mobile user location
description	Location-aware personal computing application can work accurately by estimating user location using IEEE 802.11 (Wi-Fi) signals in indoor environment. Nowadays, Wi-Fi is more and more widely available and installed on most mobile devices. Unfortunately, the Wi-Fi’s signal fluctuates greatly up to 33%, some of the causes are reflection, refraction, temperature, humidity, the dynamic environment. These make user location not in good accuracy to be estimated. In this paper we propose the use of Particle Filter to improve user location estimation which involves the modeling of non-linear and non-Gaussian systems. To make the estimation accurate, the real time data of multi-observer Wi-Fi signals is used. The loss of diversity and parameter chosen in order to reduce the ambiguity is observed in tracking user location. We improve the Particle Filter Algorithm by giving target/reference points and by reducing the computational complexity. This paper shows that the estimation of the location from real-time data is close to the real tracking object.
format	Conference or Workshop Item
author	Mantoro, Teddy Ayu, Media Anugerah Usino, Wendi Raman, Shakiratul Husna Md Latiff, Nurul Hidayati
author_facet	Mantoro, Teddy Ayu, Media Anugerah Usino, Wendi Raman, Shakiratul Husna Md Latiff, Nurul Hidayati
author_sort	Mantoro, Teddy
title	Multi-observers of particle filter approach for estimating indoor mobile user location
title_short	Multi-observers of particle filter approach for estimating indoor mobile user location
title_full	Multi-observers of particle filter approach for estimating indoor mobile user location
title_fullStr	Multi-observers of particle filter approach for estimating indoor mobile user location
title_full_unstemmed	Multi-observers of particle filter approach for estimating indoor mobile user location
title_sort	multi-observers of particle filter approach for estimating indoor mobile user location
publishDate	2011
url	http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/ http://irep.iium.edu.my/6971/1/PID2043681_ParticleFilter_Mantoro.pdf http://irep.iium.edu.my/6971/4/Multi-observers_of_Particle_Filter_Approach.pdf
first_indexed	2023-09-18T20:16:10Z
last_indexed	2023-09-18T20:16:10Z
_version_	1777407801581109248

Multi-observers of particle filter approach for estimating indoor mobile user location

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