PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios

In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional informa...

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Autores: Muthineni, Karthik|||0000-0002-8827-2013, Artemenko, Alexander, Abode, Daniel, Vidal Manzano, José|||0000-0002-1985-2065, Nájar Martón, Montserrat|||0000-0003-3507-5689
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/450713
Acceso en línea:https://hdl.handle.net/2117/450713
https://dx.doi.org/10.1109/OJVT.2025.3630970
Access Level:acceso abierto
Palabra clave:Wireless sensor networks
Wireless communication
Accuracy
Adaptation models
Graph neural networks
Data integration
Wireless fidelity
Robot sensing systems
Fingerprint recognition
Cameras
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
id ES_d49e736feb3524f01cd1fece2afae652
oai_identifier_str oai:upcommons.upc.edu:2117/450713
network_acronym_str ES
network_name_str España
repository_id_str
spelling PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenariosMuthineni, Karthik|||0000-0002-8827-2013Artemenko, AlexanderAbode, DanielVidal Manzano, José|||0000-0002-1985-2065Nájar Martón, Montserrat|||0000-0003-3507-5689Wireless sensor networksWireless communicationAccuracyAdaptation modelsGraph neural networksData integrationWireless fidelityRobot sensing systemsFingerprint recognitionCamerasÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyalÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialIn industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at 3.7-4.2GHz frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than 15cm in harsh industrial environments.This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under Grant Agreement 956670 and in part by MCIN/AEI/ 10.13039/501100011033 and ERDF A way of making Europe as part of Project 6-SENSES under Grant PID2022-138648OB-I00.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20252025-11-1020262026-01-19journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/450713https://dx.doi.org/10.1109/OJVT.2025.3630970reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 956670 Industrial Doctorate Training Network on Future Wireless Connected and Automated Industry enabled by 5GAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138648OB-I00 COMUNICACIONES 6G Y SENSADO PARA REDES INALAMBRICAS DETERMINISTASopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4507132026-05-27T15:37:01Z
dc.title.none.fl_str_mv PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
title PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
spellingShingle PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
Muthineni, Karthik|||0000-0002-8827-2013
Wireless sensor networks
Wireless communication
Accuracy
Adaptation models
Graph neural networks
Data integration
Wireless fidelity
Robot sensing systems
Fingerprint recognition
Cameras
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
title_full PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
title_fullStr PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
title_full_unstemmed PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
title_sort PosGNN: A graph neural network based multimodal data fusion for indoor positioning in industrial Non-Line-of-Sight scenarios
dc.creator.none.fl_str_mv Muthineni, Karthik|||0000-0002-8827-2013
Artemenko, Alexander
Abode, Daniel
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
author Muthineni, Karthik|||0000-0002-8827-2013
author_facet Muthineni, Karthik|||0000-0002-8827-2013
Artemenko, Alexander
Abode, Daniel
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
author_role author
author2 Artemenko, Alexander
Abode, Daniel
Vidal Manzano, José|||0000-0002-1985-2065
Nájar Martón, Montserrat|||0000-0003-3507-5689
author2_role author
author
author
author
dc.subject.none.fl_str_mv Wireless sensor networks
Wireless communication
Accuracy
Adaptation models
Graph neural networks
Data integration
Wireless fidelity
Robot sensing systems
Fingerprint recognition
Cameras
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Wireless sensor networks
Wireless communication
Accuracy
Adaptation models
Graph neural networks
Data integration
Wireless fidelity
Robot sensing systems
Fingerprint recognition
Cameras
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at 3.7-4.2GHz frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than 15cm in harsh industrial environments.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-11-10
2026
2026-01-19
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/450713
https://dx.doi.org/10.1109/OJVT.2025.3630970
url https://hdl.handle.net/2117/450713
https://dx.doi.org/10.1109/OJVT.2025.3630970
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 956670 Industrial Doctorate Training Network on Future Wireless Connected and Automated Industry enabled by 5G
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-138648OB-I00 COMUNICACIONES 6G Y SENSADO PARA REDES INALAMBRICAS DETERMINISTAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
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 Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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