Abstract
This paper deals with the problem of loaded/unloaded drones classification. Precisely, exploiting the different micro-Doppler signatures exhibited by a drone with both any load and payloads of different weights, a novel signature extraction procedure is developed for automatic recognition purposes. The developed algorithms is based on a novel adaptation of the spectral kurtosis technique to the problem at hand, specifically the analysis of narrowband and wideband spectrograms of the radar echoes reflected by the drones. In addition, the principal component analysis is used to reduce the feature vector size. The experiments conducted on measured bistatic radar data prove the effectiveness of the proposed method in separating the quoted classes of objects
Original language | English |
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Title of host publication | 2020 IEEE Radar Conference (RadarConf20) |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Electronic) | 9781728189420 |
ISBN (Print) | 9781728189437 |
DOIs | |
Publication status | Published - 4 Dec 2020 |
Event | IEEE Radar Conference 2020 - Florence, Florence, Italy Duration: 21 Sep 2020 → 25 Sep 2020 |
Conference
Conference | IEEE Radar Conference 2020 |
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Country | Italy |
City | Florence |
Period | 21/09/20 → 25/09/20 |
Keywords
- micro doppler
- automatic target recognition
- drones classification
- spectral kurtosis