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 |
|---|---|
| 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 1 |
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All Science Journal Classification (ASJC) codes
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering
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Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features. / Oh, Beom Seok; Guo, Xin; Wan, Fangyuan; Toh, Kar Ann; Lin, Zhiping.
In: IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 2, 8239598, 01.02.2018, p. 227-231.Research output: Contribution to journal › Article
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
PY - 2018/2/1
Y1 - 2018/2/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85039765853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039765853&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2017.2781711
DO - 10.1109/LGRS.2017.2781711
M3 - Article
VL - 15
SP - 227
EP - 231
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 2
M1 - 8239598
ER -