Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142959
Title: Micro-doppler mini-UAV classification using empirical-mode decomposition features
Authors: Oh, Beom-Seok
Guo, Xin
Wan, Fangyuan
Toh, Kar-Ann
Lin, Zhiping
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Oh, B.-S., Guo, X., Wan, F., Toh, K.-A., & Lin, Z. (2018). Micro-doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geoscience and Remote Sensing Letters, 15(2), 227-231. doi:10.1109/lgrs.2017.2781711
Journal: IEEE Geoscience and Remote Sensing Letters
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.
URI: https://hdl.handle.net/10356/142959
ISSN: 1545-598X
DOI: 10.1109/LGRS.2017.2781711
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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