Grid-based vessel deviation from route identification with unsupervised learning

Detalhes bibliográficos
Autor(a) principal: Antunes, N.
Data de Publicação: 2022
Outros Autores: Pereira, J., Rosa, J., Ferreira, J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/26903
Resumo: The application of anomaly-monitoring and surveillance systems is crucial for improving maritime situational awareness. These systems must work on the fly in order to provide the operator with information on potentially dangerous or illegal situations as they are occurring. We present a system for identifying vessels deviating from their normal course of travel, from unlabelled AIS data. Our approach attempts to solve problems with scalability and on-line learning of other grid-based systems available in the literature, by applying a dynamic grid size, adjustable per vessel characteristics, combined with a binary-search tree method for data discretization and vessel grid search. The results of this study have been validated during the Portuguese Maritime Trial in April 2022, conducted by the Portuguese navy along the southern coast of Portugal.
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spelling Grid-based vessel deviation from route identification with unsupervised learningVessel trajectoriesAnomaly detectionMaritime securityThe application of anomaly-monitoring and surveillance systems is crucial for improving maritime situational awareness. These systems must work on the fly in order to provide the operator with information on potentially dangerous or illegal situations as they are occurring. We present a system for identifying vessels deviating from their normal course of travel, from unlabelled AIS data. Our approach attempts to solve problems with scalability and on-line learning of other grid-based systems available in the literature, by applying a dynamic grid size, adjustable per vessel characteristics, combined with a binary-search tree method for data discretization and vessel grid search. The results of this study have been validated during the Portuguese Maritime Trial in April 2022, conducted by the Portuguese navy along the southern coast of Portugal.MDPI2022-12-29T15:19:45Z2022-01-01T00:00:00Z20222022-12-29T15:18:53Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/26903eng2076-341710.3390/app122111112Antunes, N.Pereira, J.Rosa, J.Ferreira, J.info: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:RCAAP2023-11-09T17:24:54Zoai:repositorio.iscte-iul.pt:10071/26903Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:11:18.289827Repositório 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 Grid-based vessel deviation from route identification with unsupervised learning
title Grid-based vessel deviation from route identification with unsupervised learning
spellingShingle Grid-based vessel deviation from route identification with unsupervised learning
Antunes, N.
Vessel trajectories
Anomaly detection
Maritime security
title_short Grid-based vessel deviation from route identification with unsupervised learning
title_full Grid-based vessel deviation from route identification with unsupervised learning
title_fullStr Grid-based vessel deviation from route identification with unsupervised learning
title_full_unstemmed Grid-based vessel deviation from route identification with unsupervised learning
title_sort Grid-based vessel deviation from route identification with unsupervised learning
author Antunes, N.
author_facet Antunes, N.
Pereira, J.
Rosa, J.
Ferreira, J.
author_role author
author2 Pereira, J.
Rosa, J.
Ferreira, J.
author2_role author
author
author
dc.contributor.author.fl_str_mv Antunes, N.
Pereira, J.
Rosa, J.
Ferreira, J.
dc.subject.por.fl_str_mv Vessel trajectories
Anomaly detection
Maritime security
topic Vessel trajectories
Anomaly detection
Maritime security
description The application of anomaly-monitoring and surveillance systems is crucial for improving maritime situational awareness. These systems must work on the fly in order to provide the operator with information on potentially dangerous or illegal situations as they are occurring. We present a system for identifying vessels deviating from their normal course of travel, from unlabelled AIS data. Our approach attempts to solve problems with scalability and on-line learning of other grid-based systems available in the literature, by applying a dynamic grid size, adjustable per vessel characteristics, combined with a binary-search tree method for data discretization and vessel grid search. The results of this study have been validated during the Portuguese Maritime Trial in April 2022, conducted by the Portuguese navy along the southern coast of Portugal.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-29T15:19:45Z
2022-01-01T00:00:00Z
2022
2022-12-29T15:18:53Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/26903
url http://hdl.handle.net/10071/26903
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2076-3417
10.3390/app122111112
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv MDPI
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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
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