Chebychev moments based Drone Classification, Recognition and Fingerprinting

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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
Title of host publication2021 21st International Radar Symposium (IRS)
EditorsG. Lange
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-3-944976-31-0
ISBN (Print)978-1-6654-3921-3
DOIs
Publication statusPublished - 2021
Event 2021 21st International Radar Symposium (IRS) - Online at Berlin , Germany
Duration: 21 Jun 202122 Jun 2021
Conference number: 21st

Conference

Conference 2021 21st International Radar Symposium (IRS)
CountryGermany
CityOnline at Berlin
Period21/06/2122/06/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Drones
  • UAVs
  • micro-Doppler
  • classification
  • recognition
  • fingerprinting
  • ATR
  • image moments
  • DAVs

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