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Uncertainty in radar emitter classification and clustering / Guillaume Revillon (2019)  

Public Titre : Uncertainty in radar emitter classification and clustering Titre original : Gestion des incertitudes en identification des modes radar Type de document : Thèse/HDR Auteurs : Guillaume Revillon, Auteur ; Charles Soussen, Directeur de thèse ; A. Mohammad-Djafari, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l’Université Paris-Saclay préparée à l’Université Paris-Sud Sciences et Technologies de l’Information et de la Communication (STIC) Spécialité : Traitement du signal et des images Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] approximation [Termes IGN] détection du signal [Termes IGN] écho radar [Termes IGN] émetteur [Termes IGN] estimation bayesienne [Termes IGN] inférence statistique [Termes IGN] modèle de mélange multilinéaire [Termes IGN] modulation du signal [Termes IGN] probabilités [Termes IGN] valeur aberrante Index. décimale : THESE Thèses et HDR Résumé : (auteur) In Electronic Warfare, radar signals identification is a supreme asset for decision making in military tactical situations. By providing information about the presence of threats, classification and clustering of radar signals have a significant role ensuring that countermeasures against enemies are well-chosen and enabling detection of unknown radar signals to update databases. Most of the time, Electronic Support Measures systems receive mixtures of signals from different radar emitters in the electromagnetic environment. Hence a radar signal, described by a pulse-to-pulse modulation pattern, is often partially observed due to missing measurements and measurement errors. The identification process relies on statistical analysis of basic measurable parameters of a radar signal which constitute both quantitative and qualitative data. Many general and practical approaches based on data fusion and machine learning have been developed and traditionally proceed to feature extraction, dimensionality reduction and classification or clustering. However, these algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification/clustering framework that handles both outliers and missing values for any types of data. Here, an approach based on mixture models is developed since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. The proposed approach focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering and inference is processed through a variational Bayesian approximation since the joint posterior distribution of latent variables and parameters is untractable. Some numerical experiments on synthetic and real data show that the proposed method provides more accurate results than standard algorithms. Note de contenu : Introduction 1- State of the art and the selected approach 2- Continuous data 3- Mixed data 4- Temporal evolution data 5- Conclusion and perspectives Numéro de notice : 25703 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Thales, GPI nature-HAL : Thèse DOI : sans Date de publication en ligne : 02/09/2019 En ligne : https://hal.science/tel-02275817 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94829