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Digital archive of theses discussed at the University of Pisa


Thesis etd-04092008-103620

Thesis type
Tesi di dottorato di ricerca
Thesis title
Analysis, Modelling, and Simulation of an Integrated Multisensor System for Maritime Border Control
Academic discipline
Course of study
Relatore Prof. Gini, Fulvio
Relatore Dott. Farina, Alfonso
Relatore Prof. Verrazzani, Lucio
Relatore Prof. Luise, Marco
  • classification
  • coastal surveillance
  • command and control
  • threat evaluation
Graduation session start date
Release date
In this dissertation a notional multi-sensor system acting in a maritime border control
scenario for Homeland Security (HS) is analyzed, modelled, and simulated. The functions
performed by the system are the detection, tracking, identification and classification of
naval targets that enter a sea region, the evaluation of their threat level and the selection of a suitable reaction to them. The emulated system is composed of two platforms carrying multiple sensors: a land based platform, located on the coast, and an air platform, moving on an elliptic trajectory in front of the coast. The land based platform is equipped with a Vessel Traffic Service (VTS) radar, an infrared camera (IR) and a station belonging to an Automatic Identification System (AIS). The air platform carries an Airborne Early Warning Radar (AEWR) that can operate on a spotlight Synthetic Aperture Radar (SAR) mode, a
video camera, and a second IR camera. A Command and Control (C2) centre, located on the coast, coordinates the surveillance operation. In the maritime scenario four classes of naval targets are considered: high speed dinghy, immigrant boat, fishing boat, and oil tanker. A classification algorithm is also proposed which exploits an analytical approach based on the
confusion matrix (CM) of the imaging sensors that belong to the system. The performance
of the integrated system is evaluated in terms of its Measures of Effectiveness (MoE), which
are the system metrics on the detection, classification, threat level evaluation, and selection of the intervention. These metrics are evaluated considering both the cases where an ideal error free classification process and a non-ideal classification process are performed.