CABANES, Yann; BARBARESCO, Frédéric; ARNAUDON, Marc; BIGOT, Jérémie

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CABANES, Yann
Institut de Mathématiques de Bordeaux [IMB]
Thales LAS France

BARBARESCO, Frédéric
Thales Air Systems

ARNAUDON, Marc
Institut de Mathématiques de Bordeaux [IMB]

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This paper deals with radar clutter statistical learning based on spatial Doppler fluctuation. In articles [1]-[4], data is clustered cell by cell. In this article, we generalize the previous model to extract information not only from each cell independently, but also from the cells spatial correlation. We first introduce the radar data, then the model and efficient tools to estimate the model parameters. The model parameters will be shown to be Hermitian Positive Definite Block-Toeplitz matrices. Next we endow the manifold of Hermitian Positive Definite Block-Toeplitz matrices with a Riemannian metric coming from information geometry. Finally, we adapt a supervised classification algorithm (the k-Nearest Neighbors) and an unsupervised classification algorithm (the Agglomerative Hierarchical Clustering) to this Riemannian manifold.< Réduire

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Index Terms-Radar clutter

multidimensional signals

spatio- temporal correlation

machine learning

Information geometry

Riemannian manifold

Block-Toeplitz matrices

Siegel disk

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Matrix Extension for Pathological Radar Clutter Machine Learning

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Résumé en anglais

Mots clés en anglais

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