1. A Feature-Based Approach for Loaded/Unloaded Drones Classification Exploiting micro-Doppler Signatures
- Author
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Gaetano Giunta, Carmine Clemente, Alessandro Raddi, Luca Pallotta, IEEE, Pallotta, L., Clemente, C., Raddi, A., and Giunta, G.
- Subjects
micro-Doppler ,020301 aerospace & aeronautics ,Radar tracker ,business.industry ,Computer science ,TK ,Feature vector ,Feature extraction ,automatic target recognition ,020206 networking & telecommunications ,Pattern recognition ,spectral kurtosis ,02 engineering and technology ,drones classification ,law.invention ,Bistatic radar ,Automatic target recognition ,Narrowband ,0203 mechanical engineering ,law ,0202 electrical engineering, electronic engineering, information engineering ,Spectrogram ,Artificial intelligence ,Radar ,business - 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.
- Published
- 2020
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