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Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one n...

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Detalles Bibliográficos
Autores principales: Chen, Chien-Sheng, Lin, Jium-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231337/
https://www.ncbi.nlm.nih.gov/pubmed/22163844
http://dx.doi.org/10.3390/s110404207
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author Chen, Chien-Sheng
Lin, Jium-Ming
author_facet Chen, Chien-Sheng
Lin, Jium-Ming
author_sort Chen, Chien-Sheng
collection PubMed
description Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor.
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spelling pubmed-32313372011-12-07 Applying Rprop Neural Network for the Prediction of the Mobile Station Location Chen, Chien-Sheng Lin, Jium-Ming Sensors (Basel) Communication Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor. Molecular Diversity Preservation International (MDPI) 2011-04-08 /pmc/articles/PMC3231337/ /pubmed/22163844 http://dx.doi.org/10.3390/s110404207 Text en © 2011 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 license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Communication
Chen, Chien-Sheng
Lin, Jium-Ming
Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title_full Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title_fullStr Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title_full_unstemmed Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title_short Applying Rprop Neural Network for the Prediction of the Mobile Station Location
title_sort applying rprop neural network for the prediction of the mobile station location
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231337/
https://www.ncbi.nlm.nih.gov/pubmed/22163844
http://dx.doi.org/10.3390/s110404207
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