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MS Location Estimation Based on the Artificial Bee Colony Algorithm

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm...

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Autores principales: Chen, Chien-Sheng, Huang, Jen-Fa, Huang, Nan-Chun, Chen, Kai-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582589/
https://www.ncbi.nlm.nih.gov/pubmed/33003588
http://dx.doi.org/10.3390/s20195597
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author Chen, Chien-Sheng
Huang, Jen-Fa
Huang, Nan-Chun
Chen, Kai-Sheng
author_facet Chen, Chien-Sheng
Huang, Jen-Fa
Huang, Nan-Chun
Chen, Kai-Sheng
author_sort Chen, Chien-Sheng
collection PubMed
description With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.
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spelling pubmed-75825892020-10-28 MS Location Estimation Based on the Artificial Bee Colony Algorithm Chen, Chien-Sheng Huang, Jen-Fa Huang, Nan-Chun Chen, Kai-Sheng Sensors (Basel) Article With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased. MDPI 2020-09-29 /pmc/articles/PMC7582589/ /pubmed/33003588 http://dx.doi.org/10.3390/s20195597 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Chien-Sheng
Huang, Jen-Fa
Huang, Nan-Chun
Chen, Kai-Sheng
MS Location Estimation Based on the Artificial Bee Colony Algorithm
title MS Location Estimation Based on the Artificial Bee Colony Algorithm
title_full MS Location Estimation Based on the Artificial Bee Colony Algorithm
title_fullStr MS Location Estimation Based on the Artificial Bee Colony Algorithm
title_full_unstemmed MS Location Estimation Based on the Artificial Bee Colony Algorithm
title_short MS Location Estimation Based on the Artificial Bee Colony Algorithm
title_sort ms location estimation based on the artificial bee colony algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582589/
https://www.ncbi.nlm.nih.gov/pubmed/33003588
http://dx.doi.org/10.3390/s20195597
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