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 language | English |
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Title of host publication | 2020 IEEE Radar Conference, RadarConf 2020 |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-8942-0 |
ISBN (Print) | 978-1-7281-8943-7 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 IEEE Radar Conference (RadarConf20) - Florence, Italy Duration: 21 Sept 2020 → 25 Sept 2020 |
Publication series
Name | IEEE National Radar Conference - Proceedings |
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Volume | 2020-September |
ISSN (Print) | 1097-5659 |
Conference
Conference | 2020 IEEE Radar Conference (RadarConf20) |
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Country/Territory | Italy |
City | Florence |
Period | 21/09/20 → 25/09/20 |
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-careOtherwise 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
- classification
- drones
- micro-Doppler
- staring radar
- supervised learning