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 language | English |
|---|---|
| Title of host publication | 2021 21st International Radar Symposium (IRS) |
| Editors | G. Lange |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-3-944976-31-0 |
| ISBN (Print) | 978-1-6654-3921-3 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 2021 21st International Radar Symposium (IRS) - Online at Berlin , Germany Duration: 21 Jun 2021 → 22 Jun 2021 Conference number: 21st |
Conference
| Conference | 2021 21st International Radar Symposium (IRS) |
|---|---|
| Country/Territory | Germany |
| City | Online at Berlin |
| Period | 21/06/21 → 22/06/21 |
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-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
- Drones
- UAVs
- micro-Doppler
- classification
- recognition
- fingerprinting
- ATR
- image moments
- DAVs