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Tesi etd-04092008-103620


Thesis type
Tesi di dottorato di ricerca
Author
GIOMPAPA, SOFIA
URN
etd-04092008-103620
Title
Analysis, Modelling, and Simulation of an Integrated Multisensor System for Maritime Border Control
Settore scientifico disciplinare
ING-INF/03
Corso di studi
INGEGNERIA DELL'INFORMAZIONE
Commissione
Relatore Prof. Gini, Fulvio
Relatore Dott. Farina, Alfonso
Relatore Prof. Verrazzani, Lucio
Relatore Prof. Luise, Marco
Parole chiave
  • threat evaluation
  • command and control
  • coastal surveillance
  • classification
Data inizio appello
10/06/2008;
Consultabilità
parziale
Data di rilascio
10/06/2048
Riassunto analitico
In this dissertation a notional multi-sensor system acting in a maritime border control<br>scenario for Homeland Security (HS) is analyzed, modelled, and simulated. The functions<br>performed by the system are the detection, tracking, identification and classification of<br>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<br>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<br>confusion matrix (CM) of the imaging sensors that belong to the system. The performance<br>of the integrated system is evaluated in terms of its Measures of Effectiveness (MoE), which<br>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.
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