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Micro-Doppler Signal Representation for Drone Classification by Deep Learning

Abstract : There are numerous formats which represent the micro-Doppler signature. Our goal is to determine which one is the most adapted to classify small UAV (Unmanned Aerial Vehicules) with Deep Learning. To achieve this goal, we compare drone classification results with the different micro-Doppler signatures for a given neural network. This comparison has been performed on data obtained during a radar measurement campaign. We evaluate the classification performance in function of different use conditions we identified with a given neural network. According to the experiments conducted, the recommended format is a spectrum issued from long observations as its classification results are better for most criteria.
Mots-clés : APPRENTISSAGE DRONE
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https://hal.archives-ouvertes.fr/hal-03602645
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Submitted on : Wednesday, March 9, 2022 - 11:44:53 AM
Last modification on : Wednesday, March 30, 2022 - 3:59:47 AM

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Julien Gérard, Joanna Tomasik, Christèle Morisseau, Arpad Rimmel, Gilles Vieillard. Micro-Doppler Signal Representation for Drone Classification by Deep Learning. 2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, France. ⟨10.23919/Eusipco47968.2020.9287525⟩. ⟨hal-03602645⟩

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