Pubblicazioni

Versione: 1.0 Approvato

LDO-ID 2020-04-20-01

Titolo Discrimination of air breathing targets and ballistic missiles using Data Learning

Abstract

Cognitive radar differs from traditional radar as well as from active phased array radar because of their capability in developing rules of behaviour in a self-organized manner. This is obtained by the so-called learning from experience process that results from continue interactions with the environment after a huge training phase on synthetic data. In this paper we present the results achieved applying Data Learning techniques to one of the most complex function for a surveillance radar: the classification and identification of air target with particular attention to the discrimination between air breathing targets (ABTs) and ballistic missiles (BMs).

Autori Farina Alfonso, Loffreda Massimo, Timmoneri Luca

Tipologia Paper for Seminar/Symposium/Conference

Media RadarConf 2020 - IEEE International Radar Conference (21-25 Settembre 2020, Firenze, Italy)

Sito web

Anno 2020