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Unsupervised Machine Learning for Pathological Radar Clutter Clustering: the P-Mean-Shift Algorithm

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.date.conference2019-11-19
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192030
dc.description.abstractEnThis paper deals with unsupervised radar clutter clustering to characterize pathological clutter based on their Doppler fluctuations. Operationally, being able to recognize pathological clutter environments may help to tune radar parameters to regulate the false alarm rate. This request will be more important for new generation radars that will be more mobile and should process data on the move. We first introduce the radar data structure and explain how it can be coded by Toeplitz covariance matrices. We then introduce the manifold of Toeplitz co-variance matrices and the associated metric coming from information geometry. We have adapted the classical k-means algorithm to the Riemaniann manifold of Toeplitz covariance matrices in [1], [2]; the mean-shift algorithm is presented in [3], [4]. We present here a new clustering algorithm based on the p-mean definition in a Riemannian manifold and the mean-shift algorithm.
dc.language.isoen
dc.title.enUnsupervised Machine Learning for Pathological Radar Clutter Clustering: the P-Mean-Shift Algorithm
dc.typeCommunication dans un congrès
dc.subject.halMathématiques [math]
dc.subject.halMathématiques [math]/Géométrie métrique [math.MG]
dc.subject.halMathématiques [math]/Géométrie différentielle [math.DG]
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
bordeaux.conference.titleC&ESAR 2019
bordeaux.countryFR
bordeaux.conference.cityRennes
bordeaux.peerReviewedoui
hal.identifierhal-02875430
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02875430v1
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=unknown


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