Study of the classification task into an integrated multisensor system for maritime border control

This work focuses on the classification task performed into a multi-sensor system for the coastal surveillance, composed of two platforms of sensors, which exploits an analytical approach based on the confusion matrix (CM) of the imaging sensors that belong to the system.

Abstract

This work focuses on the classification task performed into a multi-sensor system for the coastal surveillance. The system is composed of two platforms of sensors: a land based platform, equipped with a land based radar, an Automatic Identification System (AIS) and an infrared camera (IR); an airborne platform, carrying an airborne radar that can operate in a spotlight Synthetic Aperture Radar (SAR) mode, a video camera, and a second IR camera. The tasks performed by the system are the detection, tracking, identification, and classification of multiple targets, the evaluation of their threat level, and the selection of an intervention on them. The classification algorithm implemented inside the system exploits an analytical approach based on the confusion matrix (CM) of the imaging sensors that belong to the system. Some measures of effectiveness (MoE) of the system are evaluated, considering both the cases where an ideal error free classification process and a non-ideal classification process are performed.