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Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation,...

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Detalles Bibliográficos
Autores principales: Chitambira, Benny, Armour, Simon, Wales, Stephen, Beach, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263922/
https://www.ncbi.nlm.nih.gov/pubmed/30463397
http://dx.doi.org/10.3390/s18114059
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author Chitambira, Benny
Armour, Simon
Wales, Stephen
Beach, Mark
author_facet Chitambira, Benny
Armour, Simon
Wales, Stephen
Beach, Mark
author_sort Chitambira, Benny
collection PubMed
description This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.
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spelling pubmed-62639222018-12-12 Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation † Chitambira, Benny Armour, Simon Wales, Stephen Beach, Mark Sensors (Basel) Article This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better. MDPI 2018-11-20 /pmc/articles/PMC6263922/ /pubmed/30463397 http://dx.doi.org/10.3390/s18114059 Text en © 2018 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
Chitambira, Benny
Armour, Simon
Wales, Stephen
Beach, Mark
Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title_full Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title_fullStr Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title_full_unstemmed Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title_short Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation †
title_sort employing ray-tracing and least-squares support vector machines for localisation †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263922/
https://www.ncbi.nlm.nih.gov/pubmed/30463397
http://dx.doi.org/10.3390/s18114059
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