Micro-doppler mini-UAV classification using empirical-mode decomposition features

Micro-doppler mini-UAV classification using empirical-mode decomposition features

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 extr...

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Bibliographic Details
Main Authors: Oh, Beom-Seok, Guo, Xin, Wan, Fangyuan, Toh, Kar-Ann, Lin, Zhiping
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Engineering::Electrical and electronic engineering
Empirical-mode Decomposition (EMD)
Micro-Doppler Signature (m-DS)
Online Access:https://hdl.handle.net/10356/142959
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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