GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model

Detalhes bibliográficos
Autor(a) principal: Cicconet, Franciele
Data de Publicação: 2023
Outros Autores: Silva, Rui Guilherme Coelho, Mendes, P. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/85490
Resumo: Patent PCT/IB2022/053002—‘Method for Determining the Correct Placement of an Antenna with Radiation Pattern Prediction’ was originated from the work reported in this manuscript.
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spelling GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle modelAntenna placementRadiation patternAutonomous vehiclesMachine learningBayesian optimizationU-NetScience & TechnologyPatent PCT/IB2022/053002—‘Method for Determining the Correct Placement of an Antenna with Radiation Pattern Prediction’ was originated from the work reported in this manuscript.An antenna’s radiation pattern is dependent on its geometrical characteristics and its antenna’s surroundings, materials, and geometries. As such, to predict the antenna’s performance in complex environments, such as that of small antennas on large vehicles, it is necessary to obtain a model that represents such a full scenario, so that the simulation may be accomplished in the process of antenna design and placement. Due to the complex and electrically large nature of some electromagnetic problems, the detailed representation, even for a simplified model, may imply a large computational effort, both in terms of time and memory, is needed to perform the simulation. This paper evaluates how machine learning models can be used to mitigate the computational effort required to predict the behavior of antennas requiring complex modeling. It is proposed to start from a more simplified model of the electromagnetic structure to obtain a prediction for the correct solution, without needing to simulate the full structure in every iteration, and to combine this with prediction algorithms to obtain the solution of the full problem. The proposed solution uses convolutional neural networks (U-Net) of a certain accuracy to help with the correct placement of small antennas on autonomous vehicles. The standard approach requires the simulation of a full model at each test position, requiring high computational time and memory. With this new proposal, it is possible to analyze more positions and radiation patterns in a much shorter time, and with less memory, when compared with the solution from the full model. Along with this methodology for each simulation, a Bayesian optimizer is proposed to improve the search process for the best location, leading to a reduction in the required steps. This methodology was applied to support the correct positioning of a GNSS antenna with reference to a set of performance indicators required for autonomous vehicles, but it can be also applied to larger and more complex structures, allowing one to reduce the simulation time of a large electromagnetic structure and the search time for the optimum location.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) (Project number 037902; Funding Reference: POCI-01-0247-FEDER-037902).Multidisciplinary Digital Publishing InstituteUniversidade do MinhoCicconet, FrancieleSilva, Rui Guilherme CoelhoMendes, P. M.2023-02-082023-02-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85490engCicconet, F.; Silva, R.; Mendes, P.M. GNSS Antenna Pattern Prediction and Placement Optimization: A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model. Appl. Sci. 2023, 13, 2197. https://doi.org/10.3390/app130421972076-341710.3390/app13042197https://www.mdpi.com/2076-3417/13/4/2197info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:25:58Zoai:repositorium.sdum.uminho.pt:1822/85490Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:20:18.072898Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
title GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
spellingShingle GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
Cicconet, Franciele
Antenna placement
Radiation pattern
Autonomous vehicles
Machine learning
Bayesian optimization
U-Net
Science & Technology
title_short GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
title_full GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
title_fullStr GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
title_full_unstemmed GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
title_sort GNSS antenna pattern prediction and placement optimization: a prototype method using machine learning to aid complex electromagnetic simulations validated on a vehicle model
author Cicconet, Franciele
author_facet Cicconet, Franciele
Silva, Rui Guilherme Coelho
Mendes, P. M.
author_role author
author2 Silva, Rui Guilherme Coelho
Mendes, P. M.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Cicconet, Franciele
Silva, Rui Guilherme Coelho
Mendes, P. M.
dc.subject.por.fl_str_mv Antenna placement
Radiation pattern
Autonomous vehicles
Machine learning
Bayesian optimization
U-Net
Science & Technology
topic Antenna placement
Radiation pattern
Autonomous vehicles
Machine learning
Bayesian optimization
U-Net
Science & Technology
description Patent PCT/IB2022/053002—‘Method for Determining the Correct Placement of an Antenna with Radiation Pattern Prediction’ was originated from the work reported in this manuscript.
publishDate 2023
dc.date.none.fl_str_mv 2023-02-08
2023-02-08T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/85490
url https://hdl.handle.net/1822/85490
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Cicconet, F.; Silva, R.; Mendes, P.M. GNSS Antenna Pattern Prediction and Placement Optimization: A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model. Appl. Sci. 2023, 13, 2197. https://doi.org/10.3390/app13042197
2076-3417
10.3390/app13042197
https://www.mdpi.com/2076-3417/13/4/2197
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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