Simulation-based Approach to Classification of Airborne Drones

Lasse Lehmann, Jørgen Dall

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Recognition of drone type provides valuable information to assess the capability of drones, which is essential to airspace monitoring. Classification of drones on the basis of radar data is dominated by the use of supervised learning, which exploits different and often combined representations of the micro-Doppler signatures of the target. However, it is expensive and cumbersome building a catalogue of several drone micro-Doppler signatures using real data. We introduce a simulation-frame-work to generate radar data from point-scatterer targets, with associated radar cross section evaluated using physical optics. Small scale lab tests validate the fidelity of the simulated radar data, while the utility of the synthetic data for classification is tested using established methodology for classification.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Radar Conference (RadarConf20)
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Electronic)978-1-7281-8942-0
DOIs
Publication statusPublished - 2020
EventIEEE Radar Conference 2020 - Virtual event, Florence, Italy
Duration: 21 Sep 202025 Sep 2020
https://www.radarconf20.org/

Conference

ConferenceIEEE Radar Conference 2020
LocationVirtual event
CountryItaly
CityFlorence
Period21/09/202025/09/2020
Internet address
SeriesIeee National Radar Conference - Proceedings
ISSN1097-5659

Keywords

  • Micro-Doppler
  • Drone classification
  • Radar cross section modelling
  • Radar simulation
  • Machine learning

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