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An adaptive localization system using particle swarm optimization in a circular distribution form

Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique...

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Main Authors: Alhammadi, Abdulraqeb, Hashim, Fazirulhisyam, Fadlee, Mohd, Shami, Tareq M.
Format: Article
Language:English
Published: Penerbit UTM Press 2016
Online Access:http://psasir.upm.edu.my/id/eprint/55007/1/An%20adaptive%20localization%20system%20using%20particle%20swarm%20optimization%20in%20a%20circular%20distribution%20form.pdf
http://psasir.upm.edu.my/id/eprint/55007/
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spelling my.upm.eprints.550072018-07-12T10:30:13Z http://psasir.upm.edu.my/id/eprint/55007/ An adaptive localization system using particle swarm optimization in a circular distribution form Alhammadi, Abdulraqeb Hashim, Fazirulhisyam Fadlee, Mohd Shami, Tareq M. Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter. Penerbit UTM Press 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/55007/1/An%20adaptive%20localization%20system%20using%20particle%20swarm%20optimization%20in%20a%20circular%20distribution%20form.pdf Alhammadi, Abdulraqeb and Hashim, Fazirulhisyam and Fadlee, Mohd and Shami, Tareq M. (2016) An adaptive localization system using particle swarm optimization in a circular distribution form. Jurnal Teknologi, 78 (9-3). pp. 105-110. ISSN 0127–9696; ESSN: 2180–3722
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter.
format Article
author Alhammadi, Abdulraqeb
Hashim, Fazirulhisyam
Fadlee, Mohd
Shami, Tareq M.
spellingShingle Alhammadi, Abdulraqeb
Hashim, Fazirulhisyam
Fadlee, Mohd
Shami, Tareq M.
An adaptive localization system using particle swarm optimization in a circular distribution form
author_facet Alhammadi, Abdulraqeb
Hashim, Fazirulhisyam
Fadlee, Mohd
Shami, Tareq M.
author_sort Alhammadi, Abdulraqeb
title An adaptive localization system using particle swarm optimization in a circular distribution form
title_short An adaptive localization system using particle swarm optimization in a circular distribution form
title_full An adaptive localization system using particle swarm optimization in a circular distribution form
title_fullStr An adaptive localization system using particle swarm optimization in a circular distribution form
title_full_unstemmed An adaptive localization system using particle swarm optimization in a circular distribution form
title_sort adaptive localization system using particle swarm optimization in a circular distribution form
publisher Penerbit UTM Press
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/55007/1/An%20adaptive%20localization%20system%20using%20particle%20swarm%20optimization%20in%20a%20circular%20distribution%20form.pdf
http://psasir.upm.edu.my/id/eprint/55007/
_version_ 1643835771474411520
score 13.211869

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