Grid-based vessel deviation from route identification with unsupervised learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
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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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 |
format |
article |
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 |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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1799134667269472256 |