Non-supervised Machine Learning Algorithms for Radar Clutter High-Resolution Doppler Segmentation and Pathological Clutter Analysis - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Non-supervised Machine Learning Algorithms for Radar Clutter High-Resolution Doppler Segmentation and Pathological Clutter Analysis

Frédéric Barbaresco
Marc Arnaudon
Jérémie Bigot
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Résumé

Here we propose a method to classify radar clutter from radar data using a non-supervised classification algorithm. Thus new radars will be able to use the experience of other radars, which will improve their performance: 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.
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Dates et versions

hal-02875365 , version 1 (19-06-2020)

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Yann Cabanes, Frédéric Barbaresco, Marc Arnaudon, Jérémie Bigot. Non-supervised Machine Learning Algorithms for Radar Clutter High-Resolution Doppler Segmentation and Pathological Clutter Analysis. International Radar Symposium, Jun 2019, Ulm, Germany. ⟨10.23919/IRS.2019.8768140⟩. ⟨hal-02875365⟩

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