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Matrix Extension for Pathological Radar Clutter Machine Learning

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierThales LAS France
dc.contributor.authorCABANES, Yann
hal.structure.identifierThales Air Systems
dc.contributor.authorBARBARESCO, Frédéric
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorARNAUDON, Marc
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBIGOT, Jérémie
dc.date.accessioned2024-04-04T02:52:05Z
dc.date.available2024-04-04T02:52:05Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192029
dc.description.abstractEnThis 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.isoen
dc.subject.enIndex Terms-Radar clutter
dc.subject.enmultidimensional signals
dc.subject.enspatio- temporal correlation
dc.subject.enmachine learning
dc.subject.enInformation geometry
dc.subject.enRiemannian manifold
dc.subject.enBlock-Toeplitz matrices
dc.subject.enSiegel disk
dc.title.enMatrix Extension for Pathological Radar Clutter Machine Learning
dc.typeDocument de travail - Pré-publication
dc.subject.halMathématiques [math]
dc.subject.halMathématiques [math]/Géométrie différentielle [math.DG]
dc.subject.halMathématiques [math]/Géométrie métrique [math.MG]
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.subject.halStatistiques [stat]
dc.subject.halInformatique [cs]
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halPhysique [physics]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-02875440
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02875440v1
bordeaux.COinSctx_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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