Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting

Fingerprint-based indoor localization is a widely used technique for estimating the position of a device in environments where GPS is unavailable, such as inside buildings. This method maps the measured fingerprints, typically Wi-Fi signal strengths, against a database maintained by the localization...

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Detalles Bibliográficos
Autor: Laó Amores, Esperanza María
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/152082
Acceso en línea:https://hdl.handle.net/10609/152082
Access Level:acceso abierto
Palabra clave:data augmentation
RSS fingerprinting
indoor positioning
Global Positioning System -- TFM
Sistema de posicionament global -- TFM
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spelling Data augmentation models for improved indoor positioning accuracy using RSS FingerprintingLaó Amores, Esperanza Maríadata augmentationRSS fingerprintingindoor positioningGlobal Positioning System -- TFMSistema de posicionament global -- TFMFingerprint-based indoor localization is a widely used technique for estimating the position of a device in environments where GPS is unavailable, such as inside buildings. This method maps the measured fingerprints, typically Wi-Fi signal strengths, against a database maintained by the localization service provider. However, a key challenge in using RSS fingerprinting is the requirement for large amounts of data to ensure accurate positioning, which is often time-consuming and costly to collect. To address this limitation, this research explores the application of data augmentation techniques to generate additional training data, improving the performance and accuracy of indoor positioning systems. By applying various data augmentation methods, this work aims to overcome the constraints of data deficit and improve localization accuracy, particularly in complex, real-world environments where data collection is limited. The study provides a comparative analysis of different augmentation models applied to RSS fingerprint data to identify the most effective techniques for improving indoor localization. The study demonstrates that Linear Interpolation achieved substantial accuracy improvements (up to 32.16%) for structured datasets, while Generative Adversarial Networks (GANs) provided competitive performance (14.90%) and excelled in sparse scenarios. Additionally, findings highlight the importance of balancing the amount of augmented data and spatial coverage to avoid diminishing returns. These results emphasize the practical benefits of data augmentation techniques in reducing data collection costs and improving localization performance, highlighting their potential for application in diverse environments.Universitat Oberta de Catalunya (UOC)Benito Altamirano, IsmaelTorres Sospedra, Joaquín202520252024info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttps://hdl.handle.net/10609/152082reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésCC BY-NChttp://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1520822026-05-28T12:42:01Z
dc.title.none.fl_str_mv Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
title Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
spellingShingle Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
Laó Amores, Esperanza María
data augmentation
RSS fingerprinting
indoor positioning
Global Positioning System -- TFM
Sistema de posicionament global -- TFM
title_short Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
title_full Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
title_fullStr Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
title_full_unstemmed Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
title_sort Data augmentation models for improved indoor positioning accuracy using RSS Fingerprinting
dc.creator.none.fl_str_mv Laó Amores, Esperanza María
author Laó Amores, Esperanza María
author_facet Laó Amores, Esperanza María
author_role author
dc.contributor.none.fl_str_mv Benito Altamirano, Ismael
Torres Sospedra, Joaquín
dc.subject.none.fl_str_mv data augmentation
RSS fingerprinting
indoor positioning
Global Positioning System -- TFM
Sistema de posicionament global -- TFM
topic data augmentation
RSS fingerprinting
indoor positioning
Global Positioning System -- TFM
Sistema de posicionament global -- TFM
description Fingerprint-based indoor localization is a widely used technique for estimating the position of a device in environments where GPS is unavailable, such as inside buildings. This method maps the measured fingerprints, typically Wi-Fi signal strengths, against a database maintained by the localization service provider. However, a key challenge in using RSS fingerprinting is the requirement for large amounts of data to ensure accurate positioning, which is often time-consuming and costly to collect. To address this limitation, this research explores the application of data augmentation techniques to generate additional training data, improving the performance and accuracy of indoor positioning systems. By applying various data augmentation methods, this work aims to overcome the constraints of data deficit and improve localization accuracy, particularly in complex, real-world environments where data collection is limited. The study provides a comparative analysis of different augmentation models applied to RSS fingerprint data to identify the most effective techniques for improving indoor localization. The study demonstrates that Linear Interpolation achieved substantial accuracy improvements (up to 32.16%) for structured datasets, while Generative Adversarial Networks (GANs) provided competitive performance (14.90%) and excelled in sparse scenarios. Additionally, findings highlight the importance of balancing the amount of augmented data and spatial coverage to avoid diminishing returns. These results emphasize the practical benefits of data augmentation techniques in reducing data collection costs and improving localization performance, highlighting their potential for application in diverse environments.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/10609/152082
url https://hdl.handle.net/10609/152082
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv CC BY-NC
http://creativecommons.org/licenses/by-nc/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY-NC
http://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
publisher.none.fl_str_mv Universitat Oberta de Catalunya (UOC)
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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