Title: Ensemble-empirical-mode-decomposition based micro-Doppler signal separation and classification

Authors: Huajie Chen; Ping Lin; Khemraj Emrith; Pritesh Narayan; Yufeng Yao

Addresses: Key Laboratory of Fundamental Science for National Defense Communication Information Transmission and Fusion Technology, Hangzhou Dianzi University, Hangzhou, 310018, China ' Key Laboratory of Fundamental Science for National Defense Communication Information Transmission and Fusion Technology, Hangzhou Dianzi University, Hangzhou, 310018, China ' Department of Engineering Design and Mathematics, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK ' Department of Engineering Design and Mathematics, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK ' Department of Engineering Design and Mathematics, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK

Abstract: The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI) platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles.

Keywords: micro-Doppler; micro-motion; EEMD; IMF; wheeled/tracked vehicle; SAR/GMTI; signal separation; feature abstraction; vehicle classification; SVM.

DOI: 10.1504/IJCAT.2017.089089

International Journal of Computer Applications in Technology, 2017 Vol.56 No.4, pp.253 - 263

Available online: 26 Dec 2017 *

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