TY - JOUR
T1 - Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features
AU - Oh, Beom Seok
AU - Guo, Xin
AU - Wan, Fangyuan
AU - Toh, Kar Ann
AU - Lin, Zhiping
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/2
Y1 - 2018/2
N2 - 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.
AB - 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.
KW - Empirical-mode decomposition (EMD)
KW - micro-Doppler signature (m-DS)
KW - unmanned aerial vehicle (UAV) classification
UR - http://www.scopus.com/inward/record.url?scp=85039765853&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2017.2781711
DO - 10.1109/LGRS.2017.2781711
M3 - Article
AN - SCOPUS:85039765853
SN - 1545-598X
VL - 15
SP - 227
EP - 231
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 2
M1 - 8239598
ER -