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

• 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
• Institution: Nanyang Technological University

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.