Co-engineering of a radar system with mixed grey wolf optimizer: application to concealed object classification

Creators: Marot, Julien; Migliaccio, Claire; Lanteri, Jérôme; Lauga, Paul; Bourennane, Salah

Others:: Institut FRESNEL (FRESNEL) ; Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS); GSM (GSM) ; Institut FRESNEL (FRESNEL) ; Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS); École Centrale de Marseille (ECM); Laboratoire d'Electronique, Antennes et Télécommunications (LEAT) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA); IEEE

Description

The purpose of this work is to perform co-engineering of an object classification system including both a radar sensor and a software of image processing. We aim at the smallest possible false recognition rate, considering three classes of imaged objects. For this we retain five relevant parameters which impact the recognition performances. We adopt the mixed grey wolf optimizer to provide the best set of parameters.

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Co-engineering of a radar system with mixed grey wolf optimizer: application to concealed object classification

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