Entity localization and tracking: a sensor fusion-based mechanism in WSNs

Detalhes bibliográficos
Autor(a) principal: Tennina, Stefano
Data de Publicação: 2011
Outros Autores: Valletta, Marco, Santucci, Fortunato, Renzo, Marco Di, Graziosi, Fabio, Minutolo, Riccardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/3820
Resumo: Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.
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spelling Entity localization and tracking: a sensor fusion-based mechanism in WSNsLocalization and trackingInertial systemsSensor fusionKnowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.IEEERepositório Científico do Instituto Politécnico do PortoTennina, StefanoValletta, MarcoSantucci, FortunatoRenzo, Marco DiGraziosi, FabioMinutolo, Riccardo2014-02-07T15:39:48Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/3820eng978-0-7695-4538-710.1109/HPCC.2011.144metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:43:39Zoai:recipp.ipp.pt:10400.22/3820Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:24:47.188877Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Entity localization and tracking: a sensor fusion-based mechanism in WSNs
title Entity localization and tracking: a sensor fusion-based mechanism in WSNs
spellingShingle Entity localization and tracking: a sensor fusion-based mechanism in WSNs
Tennina, Stefano
Localization and tracking
Inertial systems
Sensor fusion
title_short Entity localization and tracking: a sensor fusion-based mechanism in WSNs
title_full Entity localization and tracking: a sensor fusion-based mechanism in WSNs
title_fullStr Entity localization and tracking: a sensor fusion-based mechanism in WSNs
title_full_unstemmed Entity localization and tracking: a sensor fusion-based mechanism in WSNs
title_sort Entity localization and tracking: a sensor fusion-based mechanism in WSNs
author Tennina, Stefano
author_facet Tennina, Stefano
Valletta, Marco
Santucci, Fortunato
Renzo, Marco Di
Graziosi, Fabio
Minutolo, Riccardo
author_role author
author2 Valletta, Marco
Santucci, Fortunato
Renzo, Marco Di
Graziosi, Fabio
Minutolo, Riccardo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Tennina, Stefano
Valletta, Marco
Santucci, Fortunato
Renzo, Marco Di
Graziosi, Fabio
Minutolo, Riccardo
dc.subject.por.fl_str_mv Localization and tracking
Inertial systems
Sensor fusion
topic Localization and tracking
Inertial systems
Sensor fusion
description Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-01-01T00:00:00Z
2014-02-07T15:39:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/3820
url http://hdl.handle.net/10400.22/3820
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-0-7695-4538-7
10.1109/HPCC.2011.144
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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