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.

Pallotta, L., Clemente, C., Raddi, A., Giunta, G. (2020). A Feature-Based Approach for Loaded/Unloaded Drones Classification Exploiting micro-Doppler Signatures. In IEEE National Radar Conference - Proceedings (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/RadarConf2043947.2020.9266458].

A Feature-Based Approach for Loaded/Unloaded Drones Classification Exploiting micro-Doppler Signatures

Pallotta L.
;
Giunta G.
2020

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.
978-1-7281-8942-0
Pallotta, L., Clemente, C., Raddi, A., Giunta, G. (2020). A Feature-Based Approach for Loaded/Unloaded Drones Classification Exploiting micro-Doppler Signatures. In IEEE National Radar Conference - Proceedings (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/RadarConf2043947.2020.9266458].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/376789
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 9
social impact