Abstract
In this letter, we propose an empirical-mode decomposition (EMD)-based method for automatic multicategory mini-unmanned aerial vehicle (UAV) classification. The radar echo signal is first decomposed into a set of oscillating waveforms by EMD. Then, eight statistical and geometrical features are extracted from the oscillating waveforms to capture the phenomenon of blade flashes. After feature normalization and fusion, a nonlinear support vector machine is trained for target class-label prediction. Our empirical results on real measurement of radar signals show encouraging mini-UAV classification accuracy performance.
Original language | English |
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Article number | 8239598 |
Pages (from-to) | 227-231 |
Number of pages | 5 |
Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 15 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2018 Feb |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering