Particle filter approach for tracking indoor user location using IEEE 802.11 signals

To increase the accuracy of Location-aware personal computing application, multi-observers of IEEE 802.11 (Wi-Fi) signals can be used to track indoor user location. Even-though Wi-Fi is more and more widely available on most mobile devices, unfortunately, because of the reflection, refraction, tempe...

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Main Authors:	Mantoro, Teddy, Ayu, Media Anugerah, Raman, Shakiratul Husna, Latiff , N. H. M.
Format:	Article
Language:	English
Published:	American Scientific Publishers 2011
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/6967/ http://irep.iium.edu.my/6967/ http://irep.iium.edu.my/6967/1/F1090_ParticleFilter.pdf

id	iium-6967
recordtype	eprints
spelling	iium-69672012-09-18T00:32:16Z http://irep.iium.edu.my/6967/ Particle filter approach for tracking indoor user location using IEEE 802.11 signals Mantoro, Teddy Ayu, Media Anugerah Raman, Shakiratul Husna Latiff , N. H. M. T Technology (General) To increase the accuracy of Location-aware personal computing application, multi-observers of IEEE 802.11 (Wi-Fi) signals can be used to track indoor user location. Even-though Wi-Fi is more and more widely available on most mobile devices, unfortunately, because of the reflection, refraction, temperature, humidity and the dynamic changing in the environment, the reading of Wi-Fi’s signal fluctuates greatly; the deviation can reach up to 33% from single Wi-Fi’s access point. This creates problem in tracking user location indoor. Moreover, the use of light estimation algorithms such as fingerprinting, ranking algorithm, Weighted Centroid method, k-Nearest Neighbour, did not give a good tracking result. This paper proposes the use of Particle Filter in improving user location estimation which involves the modeling of non-linear and non-Gaussian systems. The aim is to increase the accuracy of tracking user location indoor. In our experiments, the real time data of multi-observer Wi-Fi signals have been used and the loss of diversity and parameter chosen in order to reduce the ambiguity has also been observed. We improve the algorithm in reducing the computational complexity by giving target/reference points. The paper discussed the comparison between the true location and the estimated location based on two types of signals data: normal data and noise data. The location estimation is predicted based on real-time signal and then compare it to the training data set. This approach shows a promising result in tracking user location indoor using particle filter algorithm. American Scientific Publishers 2011-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/6967/1/F1090_ParticleFilter.pdf Mantoro, Teddy and Ayu, Media Anugerah and Raman, Shakiratul Husna and Latiff , N. H. M. (2011) Particle filter approach for tracking indoor user location using IEEE 802.11 signals. Advanced Science Letters. ISSN 1936-6612 (In Press) http://www.aspbs.com/science/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Mantoro, Teddy Ayu, Media Anugerah Raman, Shakiratul Husna Latiff , N. H. M. Particle filter approach for tracking indoor user location using IEEE 802.11 signals
description	To increase the accuracy of Location-aware personal computing application, multi-observers of IEEE 802.11 (Wi-Fi) signals can be used to track indoor user location. Even-though Wi-Fi is more and more widely available on most mobile devices, unfortunately, because of the reflection, refraction, temperature, humidity and the dynamic changing in the environment, the reading of Wi-Fi’s signal fluctuates greatly; the deviation can reach up to 33% from single Wi-Fi’s access point. This creates problem in tracking user location indoor. Moreover, the use of light estimation algorithms such as fingerprinting, ranking algorithm, Weighted Centroid method, k-Nearest Neighbour, did not give a good tracking result. This paper proposes the use of Particle Filter in improving user location estimation which involves the modeling of non-linear and non-Gaussian systems. The aim is to increase the accuracy of tracking user location indoor. In our experiments, the real time data of multi-observer Wi-Fi signals have been used and the loss of diversity and parameter chosen in order to reduce the ambiguity has also been observed. We improve the algorithm in reducing the computational complexity by giving target/reference points. The paper discussed the comparison between the true location and the estimated location based on two types of signals data: normal data and noise data. The location estimation is predicted based on real-time signal and then compare it to the training data set. This approach shows a promising result in tracking user location indoor using particle filter algorithm.
format	Article
author	Mantoro, Teddy Ayu, Media Anugerah Raman, Shakiratul Husna Latiff , N. H. M.
author_facet	Mantoro, Teddy Ayu, Media Anugerah Raman, Shakiratul Husna Latiff , N. H. M.
author_sort	Mantoro, Teddy
title	Particle filter approach for tracking indoor user location using IEEE 802.11 signals
title_short	Particle filter approach for tracking indoor user location using IEEE 802.11 signals
title_full	Particle filter approach for tracking indoor user location using IEEE 802.11 signals
title_fullStr	Particle filter approach for tracking indoor user location using IEEE 802.11 signals
title_full_unstemmed	Particle filter approach for tracking indoor user location using IEEE 802.11 signals
title_sort	particle filter approach for tracking indoor user location using ieee 802.11 signals
publisher	American Scientific Publishers
publishDate	2011
url	http://irep.iium.edu.my/6967/ http://irep.iium.edu.my/6967/ http://irep.iium.edu.my/6967/1/F1090_ParticleFilter.pdf
first_indexed	2023-09-18T20:16:09Z
last_indexed	2023-09-18T20:16:09Z
_version_	1777407800988663808

Particle filter approach for tracking indoor user location using IEEE 802.11 signals

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