Estimation robuste de matrices de dispersion structurées pour des modèles bien/mal spécifiés

Abstract : In most modern signal processing applications, observations are generally modeled by non-Gaussian distributions with covariance matrices exhibiting a particular structure. Taking these properties into account in the estimation scheme improves drastically the estimation accuracy. In this paper, we consider the estimation of structured scatter matrix, where the assumed model can differ from the true model of the data. Specifically, we propose a novel class of estimators, named StructurEd ScAtter Matrix Estimator (SESAME) in the mismatched framework. We also conduct a theoretical analysis of its asymptotic performance.
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Submitted on : Friday, June 14, 2019 - 8:26:27 AM
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Bruno Meriaux, Chengfang Ren, Mohammed Nabil El Korso, Arnaud Breloy, Philippe Forster. Estimation robuste de matrices de dispersion structurées pour des modèles bien/mal spécifiés. XXVIIème Colloque francophone de traitement du signal et des images (GRETSI 2019), Aug 2019, Lille, France. ⟨hal-02155887⟩

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