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Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features
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DOI：10.1109/LGRS.2017.2781711.png)
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Abstract
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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.
Keywords:
Empirical-mode decomposition (EMD)
micro-Doppler signature (m-DS)
unmanned aerial vehicle (UAV) classification
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