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Last name: ZAKY
First name: Yasmina
Encadré par Jean-Yves Dauvignac et Nicolas Fortino.
Ce travail aborde le problème de la classification d'objets à partir de leurs signatures électromagnétiques en utilisant des réseaux de neurones convolutifs (ou CNN pour Convolutional Neural Network). La classification porte à la fois sur la forme, le matériau et l'orientation spatiale de l'objet dans un repère défini par la position des...
Ce travail aborde le problème de la classification en utilisant des réseaux de neurones convolutifs (CNN) pour discriminer des objets à partir de leurs champs diffractés ultra-large bande. L'objectif est de démontrer que les données prétraitées par une méthode d'expansion en singularités (SEM) offrent une plus grande précision de...
This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on...
This paper addresses the target classification problem using supervised learning techniques to discriminate spherical objects from their scattered field. The main goal is to demonstrate that pre-processed data provide a higher accuracy for classification purposes in comparison with raw data. To this end, we compare the classification...
This paper proposes an efficient method to determine the material of spherical objects and the location of the receiving antenna relative to the object in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on...
This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on...
This study addresses the classification of objects using their electromagnetic signatures with Convolutional Neural Networks (CNNs) trained on noiseless data. The singularity expansion method (SEM) was applied to establish a compact model that accurately represents the ultra-wideband scattered field of an object, independently of its...
In this paper, different techniques for SEM poles estimation from the scattered response of an object are explored. Cauchy method and Matrix Pencil are widely used within this field, whereas Vector Fitting method is not often deployed for radar applications. Consequently, we evaluate the accuracy of these techniques applied to the simulated...