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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/177522
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:47:40Zoai: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:34982025-09-29 10:47:41.222CONICET 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 |
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1844614521208963072 |
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13.070432 |