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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/74418
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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) |
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application/pdf 1-7 |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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