Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques

Autores
Rodríguez Damián, María; Vila, Xosé A.; Rodríguez Liñares, Leandro
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.
Facultad de Informática
Materia
Ciencias Informáticas
bluetooth
indoor location
indoor positioning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/74418
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/74418
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition TechniquesPrecisión del posicionamiento en interiores utilizando Bluetooth con diferentes técnicas de reconocimiento de patronesRodríguez Damián, MaríaVila, Xosé A.Rodríguez Liñares, LeandroCiencias Informáticasbluetoothindoor locationindoor positioningObject indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.Facultad de Informática2019-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1-7http://sedici.unlp.edu.ar/handle/10915/74418enginfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.19.e01info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T09:55:33Zoai:sedici.unlp.edu.ar:10915/74418Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 09:55:33.721SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
Precisión del posicionamiento en interiores utilizando Bluetooth con diferentes técnicas de reconocimiento de patrones
title Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
spellingShingle Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
Rodríguez Damián, María
Ciencias Informáticas
bluetooth
indoor location
indoor positioning
title_short Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
title_full Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
title_fullStr Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
title_full_unstemmed Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
title_sort Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques
dc.creator.none.fl_str_mv Rodríguez Damián, María
Vila, Xosé A.
Rodríguez Liñares, Leandro
author Rodríguez Damián, María
author_facet Rodríguez Damián, María
Vila, Xosé A.
Rodríguez Liñares, Leandro
author_role author
author2 Vila, Xosé A.
Rodríguez Liñares, Leandro
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
bluetooth
indoor location
indoor positioning
topic Ciencias Informáticas
bluetooth
indoor location
indoor positioning
dc.description.none.fl_txt_mv Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.
Facultad de Informática
description Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.
publishDate 2019
dc.date.none.fl_str_mv 2019-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/74418
url http://sedici.unlp.edu.ar/handle/10915/74418
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-6038
info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.19.e01
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
1-7
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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