A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures

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Pallotta, Luca and Clemente, Carmine and Raddi, Alessandro and Giunta, Gaetano; (2020) A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures. In: 2020 IEEE Radar Conference (RadarConf20). IEEE, ITA. ISBN 9781728189420 (https://doi.org/10.1109/RadarConf2043947.2020.9266...)

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Abstract

This paper deals with the problem of loaded/unloaded drones classification. Precisely, exploiting the different micro-Doppler signatures exhibited by a drone with both any load and payloads of different weights, a novel signature extraction procedure is developed for automatic recognition purposes. The developed algorithms is based on a novel adaptation of the spectral kurtosis technique to the problem at hand, specifically the analysis of narrowband and wideband spectrograms of the radar echoes reflected by the drones. In addition, the principal component analysis is used to reduce the feature vector size. The experiments conducted on measured bistatic radar data prove the effectiveness of the proposed method in separating the quoted classes of objects

ORCID iDs

Pallotta, Luca, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X, Raddi, Alessandro and Giunta, Gaetano;
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