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Toeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric

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.accessioned2024-04-04T02:52:06Z
dc.date.available2024-04-04T02:52:06Z
dc.date.issued2019-08-27
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192032
dc.description.abstractEnHere we propose a method to classify radar clutter from radar data using an unsupervised classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz matrices and clustered using the Fisher metric. Once the clustering algorithm dispose of a large radar database, new radars will be able to use the experience of other radars, which will improve their performances: learning radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, mountains, cities; it will also improve the detectability of slow moving targets, like drones, which can be hidden in the clutter, flying close to the landform.
dc.language.isoen
dc.source.titleGeometric Science of Information
dc.subject.enBurg algorithm
dc.subject.enau- tocorrelation matrix
dc.subject.enk-means
dc.subject.enunsupervised classification
dc.subject.enmachine learning
dc.subject.enradar clutter
dc.subject.enKähler metric
dc.subject.enreflection coefficients
dc.title.enToeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1007/978-3-030-26980-7_27
dc.subject.halMathématiques [math]
dc.subject.halMathématiques [math]/Géométrie métrique [math.MG]
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halInformatique [cs]
dc.subject.halStatistiques [stat]
dc.subject.halPhysique [physics]
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
bordeaux.page261-270
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingGeometric Science of Information
hal.identifierhal-02875403
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02875403v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Geometric%20Science%20of%20Information&rft.date=2019-08-27&rft.spage=261-270&rft.epage=261-270&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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