Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features

Beom Seok Oh; Xin Guo; Fangyuan Wan; Kar Ann Toh; Zhiping Lin

doi:10.1109/LGRS.2017.2781711

Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features

Beom Seok Oh, Xin Guo, Fangyuan Wan, Kar Ann Toh, Zhiping Lin

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article

23 Citations (Scopus)

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	https://doi.org/10.1109/LGRS.2017.2781711
Publication status	Published - 2018 Feb

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All Science Journal Classification (ASJC) codes

Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering

Cite this

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. [8239598]. https://doi.org/10.1109/LGRS.2017.2781711

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Access to Document

10.1109/LGRS.2017.2781711