Chebychev moments based drone classification, recognition and fingerprinting

Carmine Clemente, Luca Pallotta, Christos Ilioudis, Francesco Fioranelli, Gaetano Giunta, Alfonso Farina

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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.
Original languageEnglish
Number of pages6
Publication statusPublished - 22 Jun 2021
EventInternational Radar Symposium - Online, Bonn, Germany
Duration: 21 Jun 202122 Jun 2022
https://www.dgon-irs.org/home/

Conference

ConferenceInternational Radar Symposium
Abbreviated titleIRS 2021
Country/TerritoryGermany
CityBonn
Period21/06/2122/06/22
Internet address

Keywords

  • drones
  • UAVs
  • micro-Doppler
  • classification
  • recognition
  • fingerprinting
  • ATR
  • image moments

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