Dynamic indoor localization using maximum likelihood particle filtering

Autores
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.
Fil: Wang, Wenxu. Guangdong University of Technology; China
Fil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; China
Materia
CHANNEL STATE INFORMATION
INDOOR TRACKING
PARTICLE FILTER
WIFI FINGERPRINTING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/177522
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/177522
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Dynamic indoor localization using maximum likelihood particle filteringWang, WenxuMarelli, Damian EdgardoFu, MinyueCHANNEL STATE INFORMATIONINDOOR TRACKINGPARTICLE FILTERWIFI FINGERPRINTINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.Fil: Wang, Wenxu. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; ChinaMolecular Diversity Preservation International2021-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/177522Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Dynamic indoor localization using maximum likelihood particle filtering; Molecular Diversity Preservation International; Sensors; 21; 4; 2-2021; 1-181424-8220CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/s21041090info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/21/4/1090info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:21:11Zoai:ri.conicet.gov.ar:11336/177522instacron: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:34982025-10-22 12:21:11.727CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dynamic indoor localization using maximum likelihood particle filtering
title Dynamic indoor localization using maximum likelihood particle filtering
spellingShingle Dynamic indoor localization using maximum likelihood particle filtering
Wang, Wenxu
CHANNEL STATE INFORMATION
INDOOR TRACKING
PARTICLE FILTER
WIFI FINGERPRINTING
title_short Dynamic indoor localization using maximum likelihood particle filtering
title_full Dynamic indoor localization using maximum likelihood particle filtering
title_fullStr Dynamic indoor localization using maximum likelihood particle filtering
title_full_unstemmed Dynamic indoor localization using maximum likelihood particle filtering
title_sort Dynamic indoor localization using maximum likelihood particle filtering
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 CHANNEL STATE INFORMATION
INDOOR TRACKING
PARTICLE FILTER
WIFI FINGERPRINTING
topic CHANNEL STATE INFORMATION
INDOOR TRACKING
PARTICLE FILTER
WIFI FINGERPRINTING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.
Fil: Wang, Wenxu. Guangdong University of Technology; China
Fil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; China
description A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.
publishDate 2021
dc.date.none.fl_str_mv 2021-02
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/11336/177522
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Dynamic indoor localization using maximum likelihood particle filtering; Molecular Diversity Preservation International; Sensors; 21; 4; 2-2021; 1-18
1424-8220
CONICET Digital
CONICET
url http://hdl.handle.net/11336/177522
identifier_str_mv Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Dynamic indoor localization using maximum likelihood particle filtering; Molecular Diversity Preservation International; Sensors; 21; 4; 2-2021; 1-18
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/s21041090
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/21/4/1090
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/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_ 1846782664062795776
score 12.982451

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