Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel mo...

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Detalhes bibliográficos
Autores: Castro-Arvizu, JM, Vilá-Valls, J, Moragrega, A, Closas, P, Fernandez-Rubio, JA
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Recursos:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p1235
Acesso em linha:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1235
Access Level:acceso abierto
Palavra-chave:Indoor localization
wireless sensor networks
robust filtering
two-slope path loss model
channel model calibration
received signal strength
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spelling Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithmCastro-Arvizu, JMVilá-Valls, JMoragrega, AClosas, PFernandez-Rubio, JAIndoor localizationwireless sensor networksrobust filteringtwo-slope path loss modelchannel model calibrationreceived signal strengthDue to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model-based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.SAGE Publications Inc.2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1235INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKSISSN: 15501329ISSNe: 15501477reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p12352026-06-17T11:44:47Z
dc.title.none.fl_str_mv Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
title Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
spellingShingle Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
Castro-Arvizu, JM
Indoor localization
wireless sensor networks
robust filtering
two-slope path loss model
channel model calibration
received signal strength
title_short Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
title_full Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
title_fullStr Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
title_full_unstemmed Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
title_sort Received signal strength-based indoor localization using a robust interacting multiple model-extended Kalman filter algorithm
dc.creator.none.fl_str_mv Castro-Arvizu, JM
Vilá-Valls, J
Moragrega, A
Closas, P
Fernandez-Rubio, JA
author Castro-Arvizu, JM
author_facet Castro-Arvizu, JM
Vilá-Valls, J
Moragrega, A
Closas, P
Fernandez-Rubio, JA
author_role author
author2 Vilá-Valls, J
Moragrega, A
Closas, P
Fernandez-Rubio, JA
author2_role author
author
author
author
dc.subject.none.fl_str_mv Indoor localization
wireless sensor networks
robust filtering
two-slope path loss model
channel model calibration
received signal strength
topic Indoor localization
wireless sensor networks
robust filtering
two-slope path loss model
channel model calibration
received signal strength
description Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model-based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1235
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1235
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv SAGE Publications Inc.
publisher.none.fl_str_mv SAGE Publications Inc.
dc.source.none.fl_str_mv INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
ISSN: 15501329
ISSNe: 15501477
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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