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

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.