ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-04122022-200111


Tipo di tesi
Tesi di dottorato di ricerca
Autore
MILLEFIORI, LEONARDO MARIA
URN
etd-04122022-200111
Titolo
Machine Learning and Data Fusion Methods for Enhanced Maritime Surveillance
Settore scientifico disciplinare
ING-INF/03
Corso di studi
INGEGNERIA DELL'INFORMAZIONE
Relatori
tutor Prof. Gini, Fulvio
Parole chiave
  • maritime surveillance
  • machine learning
  • data fusion
  • target tracking
  • target motion models
  • signal processing
  • COVID-19
  • maritime traffic
  • shipping
  • maritime traffic patterns
Data inizio appello
09/05/2022
Consultabilità
Non consultabile
Data di rilascio
09/05/2062
Riassunto
The monitoring of activities at sea is a key enabler for an effective Maritime Situational Awareness (MSA) capability. MSA can be thought as the means by which the operational picture is formed, which is constantly and continuously updated with data and information originating from deployed units and surveillance networks, both terrestrial and space-based. The achievement of an advanced MSA capability on the global scale remains an only partially solved challenge. This thesis offers advancements of machine learning and data fusion techniques, aiming at efficiently processing vast amounts of information with the final goal of supporting decision processes. Specifically, analytics methods are proposed to predict future vessel positions even in complex maritime traffic environments, thanks to the use of signal processing and deep-learning techniques jointly with large volumes of historical traffic data. Based on the prediction model, maritime anomaly detection methodologies are then developed to determine if a target deviates from a standard route even in the case of no data available, i.e., when the vessel goes “dark.” Finally, an assessment of the COVID-19 impact on the maritime commercial traffic based on big data processing is also presented.
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