This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels of major classification steps, namely classification, recognition and fingerprinting, demonstrating the effectiveness of the proposed approach to discriminate drones from birds, fixed wings from multi-rotors and drones carrying different payloads on real measured radar data.

Clemente, C., Pallotta, L., Ilioudis, C., Fioranelli, F., Giunta, G., Farina, A. (2021). Chebychev moments based Drone Classification, Recognition and Fingerprinting. In Proceedings International Radar Symposium (pp.1-6). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE Computer Society [10.23919/IRS51887.2021.9466211].

Chebychev moments based Drone Classification, Recognition and Fingerprinting

Pallotta L.;Giunta G.;
2021-01-01

Abstract

This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels of major classification steps, namely classification, recognition and fingerprinting, demonstrating the effectiveness of the proposed approach to discriminate drones from birds, fixed wings from multi-rotors and drones carrying different payloads on real measured radar data.
2021
978-3-944976-31-0
Clemente, C., Pallotta, L., Ilioudis, C., Fioranelli, F., Giunta, G., Farina, A. (2021). Chebychev moments based Drone Classification, Recognition and Fingerprinting. In Proceedings International Radar Symposium (pp.1-6). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE Computer Society [10.23919/IRS51887.2021.9466211].
File in questo prodotto:
File Dimensione Formato  
60_Clemente_IRS_2021_Chebychev_moments_based_Drone_Classification.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Documento in Post-print
Licenza: DRM non definito
Dimensione 163.7 kB
Formato Adobe PDF
163.7 kB Adobe PDF Visualizza/Apri

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/11590/392830
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact