Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter - Archive ouverte HAL
Communication Dans Un Congrès Année : 2020

Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter

Frédéric Barbaresco
Marc Arnaudon
Jérémie Bigot
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Résumé

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.
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Dates et versions

hal-02875415 , version 1 (19-06-2020)

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Citer

Yann Cabanes, Frédéric Barbaresco, Marc Arnaudon, Jérémie Bigot. Non-Supervised High Resolution Doppler Machine Learning for Pathological Radar Clutter. RADAR 2019, Sep 2020, Toulon, France. ⟨10.1109/RADAR41533.2019.171295⟩. ⟨hal-02875415⟩

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