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Pré-Publication, Document De Travail Année : 2020

Matrix Extension for Pathological Radar Clutter Machine Learning

Frédéric Barbaresco
Marc Arnaudon
Jérémie Bigot
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Résumé

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.
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Dates et versions

hal-02875440 , version 1 (19-06-2020)

Identifiants

  • HAL Id : hal-02875440 , version 1

Citer

Yann Cabanes, Frédéric Barbaresco, Marc Arnaudon, Jérémie Bigot. Matrix Extension for Pathological Radar Clutter Machine Learning. 2020. ⟨hal-02875440⟩

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