Indoor Positioning System using Dynamic Model Estimation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFAM |
Texto Completo: | https://tede.ufam.edu.br/handle/tede/8241 |
Resumo: | Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using static model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this work, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the propagation model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analyses executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% lower than the position estimates obtained by positioning models based on static parameters. |
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Indoor Positioning System using Dynamic Model EstimationSistema de Posicionamento Interno usando Estimativa Dinâmica de ModeloMobile devicesIndoor environmentsDynamic Model EstimationDynamic parametersBluetooth-basedCIÊNCIAS EXATAS E DA TERRAIndoor Positioning SystemBluetooth Low EnergyPath-loss ModelLocalization SystemTrilaterationIndoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using static model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this work, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the propagation model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analyses executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% lower than the position estimates obtained by positioning models based on static parameters.Os sistemas de posicionamento interno (IPSs) são usados para localizar dispositivos móveis em ambientes internos. IPSs baseados em modelo de propagação têm a vantagem de não ter um treinamento exaustivo e uma extensa caracterização de sinal do ambiente, conforme exigido pela técnica de impressão digital. No entanto, a maioria dos IPSs baseados em modelo utilizam parâmetros de perda de sinal fixos, tratando todo o cenário como tendo um sinal uniforme de propagação. Isso pode funcionar para os experimentos em pequena escala, mas não para cenários maiores, como escolas, shoppings e hospitais. Neste trabalho propomos o PoDME (Posicionamento usando estimativa de modelo dinâmico, do inglês, Positioning using Dynamic Model Estimation), um IPS baseado em modelo que usa parâmetros dinâmicos que são estimados com base na região em que o sinal foi enviado. Mais especificamente, usamos o conjunto de nós âncoras que recebem o sinal enviado por um dispositivo móvel e suas intensidades de sinal, para estimar os melhores valores locais para os parâmetros do modelo log-distance. Além disso, uma vez que nossa solução depende muito dos nós âncoras selecionados para usar no cálculo da posição, propomos um novo método para escolher os três melhores nós âncoras, não escolhendo apenas os nós mais próximos, mas também aqueles que beneficiam o cálculo de posição com base em mínimos quadrados. O método proposto é baseado na tecnologia Bluetooth Low Energy (BLE) e realizamos várias análises de dados com experimentos em um cenário real de grande escala. Os resultados preliminares mostram que a nossa solução atinge um erro de estimativa de posição de 3 m, que é 17% melhor do que IPSs que utilizam modelos com parâmetros fixos.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade Federal do AmazonasInstituto de ComputaçãoBrasilUFAMPrograma de Pós-graduação em InformáticaOliveira, Horácio Antonio Braga Fernandes dehttp://lattes.cnpq.br/9314744999783676Souto, Eduardo James Pereirahttp://lattes.cnpq.br/3875301617975895Pazzi, Richard Werner Nelemhttp://lattes.cnpq.br/6341366360323491Assayag, Yuri Freitashttp://lattes.cnpq.br/6409128880667607https://orcid.org/0000-0002-1612-306X2021-04-28T21:38:49Z2021-03-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfASSAYAG, Yuri Freitas. Indoor Positioning System using Dynamic Model Estimation. 2021. 40 f. Dissertação (Mestrado em Informática) - Universidade Federal do Amazonas, Manaus, 2021.https://tede.ufam.edu.br/handle/tede/8241enghttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFAMinstname:Universidade Federal do Amazonas (UFAM)instacron:UFAM2021-04-29T05:03:54Zoai:https://tede.ufam.edu.br/handle/:tede/8241Biblioteca Digital de Teses e Dissertaçõeshttp://200.129.163.131:8080/PUBhttp://200.129.163.131:8080/oai/requestddbc@ufam.edu.br||ddbc@ufam.edu.bropendoar:65922021-04-29T05:03:54Biblioteca Digital de Teses e Dissertações da UFAM - Universidade Federal do Amazonas (UFAM)false |
dc.title.none.fl_str_mv |
Indoor Positioning System using Dynamic Model Estimation Sistema de Posicionamento Interno usando Estimativa Dinâmica de Modelo |
title |
Indoor Positioning System using Dynamic Model Estimation |
spellingShingle |
Indoor Positioning System using Dynamic Model Estimation Assayag, Yuri Freitas Mobile devices Indoor environments Dynamic Model Estimation Dynamic parameters Bluetooth-based CIÊNCIAS EXATAS E DA TERRA Indoor Positioning System Bluetooth Low Energy Path-loss Model Localization System Trilateration |
title_short |
Indoor Positioning System using Dynamic Model Estimation |
title_full |
Indoor Positioning System using Dynamic Model Estimation |
title_fullStr |
Indoor Positioning System using Dynamic Model Estimation |
title_full_unstemmed |
Indoor Positioning System using Dynamic Model Estimation |
title_sort |
Indoor Positioning System using Dynamic Model Estimation |
author |
Assayag, Yuri Freitas |
author_facet |
Assayag, Yuri Freitas http://lattes.cnpq.br/6409128880667607 https://orcid.org/0000-0002-1612-306X |
author_role |
author |
author2 |
http://lattes.cnpq.br/6409128880667607 https://orcid.org/0000-0002-1612-306X |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Oliveira, Horácio Antonio Braga Fernandes de http://lattes.cnpq.br/9314744999783676 Souto, Eduardo James Pereira http://lattes.cnpq.br/3875301617975895 Pazzi, Richard Werner Nelem http://lattes.cnpq.br/6341366360323491 |
dc.contributor.author.fl_str_mv |
Assayag, Yuri Freitas http://lattes.cnpq.br/6409128880667607 https://orcid.org/0000-0002-1612-306X |
dc.subject.por.fl_str_mv |
Mobile devices Indoor environments Dynamic Model Estimation Dynamic parameters Bluetooth-based CIÊNCIAS EXATAS E DA TERRA Indoor Positioning System Bluetooth Low Energy Path-loss Model Localization System Trilateration |
topic |
Mobile devices Indoor environments Dynamic Model Estimation Dynamic parameters Bluetooth-based CIÊNCIAS EXATAS E DA TERRA Indoor Positioning System Bluetooth Low Energy Path-loss Model Localization System Trilateration |
description |
Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using static model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this work, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the propagation model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analyses executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% lower than the position estimates obtained by positioning models based on static parameters. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-04-28T21:38:49Z 2021-03-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
ASSAYAG, Yuri Freitas. Indoor Positioning System using Dynamic Model Estimation. 2021. 40 f. Dissertação (Mestrado em Informática) - Universidade Federal do Amazonas, Manaus, 2021. https://tede.ufam.edu.br/handle/tede/8241 |
identifier_str_mv |
ASSAYAG, Yuri Freitas. Indoor Positioning System using Dynamic Model Estimation. 2021. 40 f. Dissertação (Mestrado em Informática) - Universidade Federal do Amazonas, Manaus, 2021. |
url |
https://tede.ufam.edu.br/handle/tede/8241 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Amazonas Instituto de Computação Brasil UFAM Programa de Pós-graduação em Informática |
publisher.none.fl_str_mv |
Universidade Federal do Amazonas Instituto de Computação Brasil UFAM Programa de Pós-graduação em Informática |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFAM instname:Universidade Federal do Amazonas (UFAM) instacron:UFAM |
instname_str |
Universidade Federal do Amazonas (UFAM) |
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UFAM |
institution |
UFAM |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFAM |
collection |
Biblioteca Digital de Teses e Dissertações da UFAM |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da UFAM - Universidade Federal do Amazonas (UFAM) |
repository.mail.fl_str_mv |
ddbc@ufam.edu.br||ddbc@ufam.edu.br |
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1809732043553112064 |