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

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

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)


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 languageEnglish
Article number8239598
Pages (from-to)227-231
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number2
Publication statusPublished - 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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