Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking

Autores
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
Fil: Wang, Wenxu. Guandong University Of Technology; China
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Fu, Minyue. Universidad de Newcastle; Australia
Materia
BAYESIAN TRACKING
CSI
FINGERPRINTING
INDOOR LOCALIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/183504
Acceder
id CONICETDig_bc573f5ef74dd0ac7779db04cfbdc594
oai_identifier_str oai:ri.conicet.gov.ar:11336/183504
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian trackingWang, WenxuMarelli, Damian EdgardoFu, MinyueBAYESIAN TRACKINGCSIFINGERPRINTINGINDOOR LOCALIZATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.Fil: Wang, Wenxu. Guandong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; AustraliaMolecular Diversity Preservation International2020-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/183504Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking; Molecular Diversity Preservation International; Sensors; 20; 10; 7-2020; 1-151424-8220CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/s20102854info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:50Zoai:ri.conicet.gov.ar:11336/183504instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:45:50.515CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
title Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
spellingShingle Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
Wang, Wenxu
BAYESIAN TRACKING
CSI
FINGERPRINTING
INDOOR LOCALIZATION
title_short Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
title_full Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
title_fullStr Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
title_full_unstemmed Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
title_sort Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking
dc.creator.none.fl_str_mv Wang, Wenxu
Marelli, Damian Edgardo
Fu, Minyue
author Wang, Wenxu
author_facet Wang, Wenxu
Marelli, Damian Edgardo
Fu, Minyue
author_role author
author2 Marelli, Damian Edgardo
Fu, Minyue
author2_role author
author
dc.subject.none.fl_str_mv BAYESIAN TRACKING
CSI
FINGERPRINTING
INDOOR LOCALIZATION
topic BAYESIAN TRACKING
CSI
FINGERPRINTING
INDOOR LOCALIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
Fil: Wang, Wenxu. Guandong University Of Technology; China
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Fu, Minyue. Universidad de Newcastle; Australia
description Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
publishDate 2020
dc.date.none.fl_str_mv 2020-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/183504
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking; Molecular Diversity Preservation International; Sensors; 20; 10; 7-2020; 1-15
1424-8220
CONICET Digital
CONICET
url http://hdl.handle.net/11336/183504
identifier_str_mv Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking; Molecular Diversity Preservation International; Sensors; 20; 10; 7-2020; 1-15
1424-8220
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/s20102854
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613433124716544
score 13.070432

Publicaciones similares