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 › peer-review

40 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

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Geotechnical Engineering and Engineering Geology
Electrical and Electronic Engineering

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10.1109/LGRS.2017.2781711

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author = "Oh, {Beom Seok} and Xin Guo and Fangyuan Wan and Toh, {Kar Ann} and Zhiping Lin",

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AU - Wan, Fangyuan

AU - Toh, Kar Ann

AU - Lin, Zhiping

PY - 2018/2

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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.

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

Abstract

Bibliographical note

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