Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar

Cameron Bennet, Mohammad Jahangir, Francesco Fioranelli, Bashar I Ahmad, Julien Le Kernec

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

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Abstract

The commercialization of drones has granted the public with unprecedented access to unmanned aviation. As such, the detection, tracking, and classification of drones in radars have become an area in high demand to mitigate accidental or voluntary misuse of these platforms. This paper focuses on the classification of drone targets in a safety context where the concept of Explainable AI is of particular interest. Here, we propose a simple, yet effective, means to extract a salient symmetry feature from the micro-Doppler signatures of drone targets, arising from onboard rotary components. Most importantly, this approach maintains the explainable nature of the employed recognition algorithm as the symmetry feature is directly related to the kinematics of the drones as the targets of interest. A large dataset collected from multiple locations with over 280 minutes of rotary and fixed wing drone flights has been collected and used to demonstrate the generalization capability of this approach.
Original languageEnglish
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
Place of PublicationPiscataway
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-7281-8942-0
ISBN (Print)978-1-7281-8943-7
DOIs
Publication statusPublished - 2020
Event2020 IEEE Radar Conference (RadarConf20) - Florence, Italy
Duration: 21 Sep 202025 Sep 2020

Publication series

NameIEEE National Radar Conference - Proceedings
Volume2020-September
ISSN (Print)1097-5659

Conference

Conference2020 IEEE Radar Conference (RadarConf20)
CountryItaly
CityFlorence
Period21/09/2025/09/20

Keywords

  • classification
  • drones
  • micro-Doppler
  • staring radar
  • supervised learning

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