Matrix Extension for Pathological Radar Clutter Machine Learning
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
hal.structure.identifier | Thales LAS France | |
dc.contributor.author | CABANES, Yann | |
hal.structure.identifier | Thales Air Systems | |
dc.contributor.author | BARBARESCO, Frédéric | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | ARNAUDON, Marc | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | BIGOT, Jérémie | |
dc.date.accessioned | 2024-04-04T02:52:05Z | |
dc.date.available | 2024-04-04T02:52:05Z | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192029 | |
dc.description.abstractEn | 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. | |
dc.language.iso | en | |
dc.subject.en | Index Terms-Radar clutter | |
dc.subject.en | multidimensional signals | |
dc.subject.en | spatio- temporal correlation | |
dc.subject.en | machine learning | |
dc.subject.en | Information geometry | |
dc.subject.en | Riemannian manifold | |
dc.subject.en | Block-Toeplitz matrices | |
dc.subject.en | Siegel disk | |
dc.title.en | Matrix Extension for Pathological Radar Clutter Machine Learning | |
dc.type | Document de travail - Pré-publication | |
dc.subject.hal | Mathématiques [math] | |
dc.subject.hal | Mathématiques [math]/Géométrie différentielle [math.DG] | |
dc.subject.hal | Mathématiques [math]/Géométrie métrique [math.MG] | |
dc.subject.hal | Statistiques [stat]/Machine Learning [stat.ML] | |
dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
dc.subject.hal | Statistiques [stat] | |
dc.subject.hal | Informatique [cs] | |
dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
dc.subject.hal | Physique [physics] | |
bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
hal.identifier | hal-02875440 | |
hal.version | 1 | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-02875440v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=CABANES,%20Yann&BARBARESCO,%20Fr%C3%A9d%C3%A9ric&ARNAUDON,%20Marc&BIGOT,%20J%C3%A9r%C3%A9mie&rft.genre=preprint |
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