Ritchie, M;
(2021)
Multi-Frequency Micro-Doppler Based Classification Of Micro-Drone Payload Weight.
Frontiers in Signal Processing
, 1
, Article 781777. 10.3389/frsip.2021.781777.
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Abstract
The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.
| Type: | Article |
|---|---|
| Title: | Multi-Frequency Micro-Doppler Based Classification Of Micro-Drone Payload Weight |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/frsip.2021.781777 |
| Publisher version: | https://doi.org/10.3389/frsip.2021.781777 |
| Language: | English |
| Additional information: | © 2021 Dhulashia, Peters, Horne, Beasley and Ritchie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | micro-drone, multi-frequency radar, micro-Doppler, payload classification, counter-UAV, Doppler signatures, multi-functional radar |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10134023 |
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