Deep learning to enhance maritime situation awareness

Mantecón del Valle, Tomás and Casals Tortosa, David and Navarro Corcuera, Juan José and Blanco Adán, Carlos Roberto del and Jaureguizar Núñez, Fernando (2019). Deep learning to enhance maritime situation awareness. In: "20th International Radar Symposium (IRS) 2019", 26/06/2019 - 28/06/2019, Ulm, Alemania. ISBN 978-1-7281-0421-8. pp. 1-8. https://doi.org/10.23919/IRS.2019.8768142.

Description

Title: Deep learning to enhance maritime situation awareness
Author/s:
  • Mantecón del Valle, Tomás
  • Casals Tortosa, David
  • Navarro Corcuera, Juan José
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
Item Type: Presentation at Congress or Conference (Article)
Event Title: 20th International Radar Symposium (IRS) 2019
Event Dates: 26/06/2019 - 28/06/2019
Event Location: Ulm, Alemania
Title of Book: Proceedings of 20th International Radar Symposium (IRS) 2019
Título de Revista/Publicación: 2019 20TH INTERNATIONAL RADAR SYMPOSIUM (IRS)
Date: 22 July 2019
ISBN: 978-1-7281-0421-8
ISSN: 2155-5745
Subjects:
Freetext Keywords: data handling, decision support systems, learning (artificial intelligence), marine navigation,marine radar, marine safety, marine vehicles, military computing, neural nets, pattern classification, sensor fusion, surveillance
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Maritime surveillance sensors like AIS (Automatic Identification System) and Radar provide useful information for decision-making support, which is of paramount importance for effective operations against maritime threats and illegal activities [1]. However, decision-making systems that trust solely on AIS information tend to fail in real situations because such information could be missing, inaccurate or even deceptive [2]. On the other hand, only Radar information is not enough to get a complete description of the maritime situational picture. This paper proposes a deep learning framework for vessel monitoring that examines a particular scenario where a deep learning solution can infer a navigation status based on the vessels trajectories, and thus to detect suspicious vessels activities. For this purpose, a dataset, named DeepMarine, has been specifically created by collecting data of AIS historical recordings. We demonstrate the performance of the developed deep learning framework for the proposed vessels activity classification, which can be ultimately used to report illegal activities.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2016-75981IVMEUnspecifiedUnspecified

More information

Item ID: 64243
DC Identifier: https://oa.upm.es/64243/
OAI Identifier: oai:oa.upm.es:64243
DOI: 10.23919/IRS.2019.8768142
Official URL: https://ieeexplore.ieee.org/document/8768142
Deposited by: Memoria Investigacion
Deposited on: 09 May 2021 08:17
Last Modified: 09 May 2021 08:17
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