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Chapitre D'ouvrage Année : 2019

Toeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric

Frédéric Barbaresco
Marc Arnaudon
Jérémie Bigot
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Résumé

Here 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.
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Dates et versions

hal-02875403 , version 1 (19-06-2020)

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Yann Cabanes, Frédéric Barbaresco, Marc Arnaudon, Jérémie Bigot. Toeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric. Geometric Science of Information, pp.261-270, 2019, ⟨10.1007/978-3-030-26980-7_27⟩. ⟨hal-02875403⟩

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