Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783599226130792448 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7582589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenchiensheng mslocationestimationbasedontheartificialbeecolonyalgorithm AT huangjenfa mslocationestimationbasedontheartificialbeecolonyalgorithm AT huangnanchun mslocationestimationbasedontheartificialbeecolonyalgorithm AT chenkaisheng mslocationestimationbasedontheartificialbeecolonyalgorithm |