Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter
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 | 2024-04-04T02:52:06Z | |
dc.date.available | 2024-04-04T02:52:06Z | |
dc.date.conference | 2020-09-23 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192031 | |
dc.description.abstractEn | In this paper we propose a method to classify radar clutter from radar data using a non-supervised classification algorithm. As a final objective, new radars will therefore be able to use the experience of other radars to improve their performances: learning pathological 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.iso | en | |
dc.source.title | 2019 International Radar Conference (RADAR) | |
dc.subject.en | non-supervised classification | |
dc.subject.en | machine learning | |
dc.subject.en | radar clutter | |
dc.subject.en | k-means | |
dc.subject.en | autocorrelation matrix | |
dc.subject.en | Burg algorithm | |
dc.subject.en | reflection coefficients | |
dc.subject.en | Kähler metric | |
dc.title.en | Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter | |
dc.type | Communication dans un congrès | |
dc.identifier.doi | 10.1109/RADAR41533.2019.171295 | |
dc.subject.hal | Mathématiques [math] | |
dc.subject.hal | Mathématiques [math]/Géométrie métrique [math.MG] | |
dc.subject.hal | Mathématiques [math]/Géométrie différentielle [math.DG] | |
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 | |
bordeaux.conference.title | RADAR 2019 | |
bordeaux.country | FR | |
bordeaux.title.proceeding | 2019 International Radar Conference (RADAR) | |
bordeaux.conference.city | Toulon | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-02875415 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | oui | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-02875415v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=2019%20International%20Radar%20Conference%20(RADAR)&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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