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Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

Chen, H.; Lin, P.; Emrith, Khemraj; Narayan, Pritesh P; Yao, Yufeng

Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification Thumbnail


Authors

H. Chen

P. Lin

Dr Khemraj Emrith Khemraj.Emrith@uwe.ac.uk
Associate Head of Departmemt Business Engagement and Partnerships

Yufeng Yao Yufeng.Yao@uwe.ac.uk
Professor in Aerospace Engineering



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.

Citation

Chen, H., Lin, P., Emrith, K., Narayan, P. P., & Yao, Y. (2017). Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification. International Journal of Computer Applications in Technology, 56(4), 253-263. https://doi.org/10.1504/IJCAT.2017.10009946

Journal Article Type Article
Acceptance Date Jan 10, 2017
Online Publication Date Dec 26, 2017
Publication Date Nov 24, 2017
Publicly Available Date Nov 25, 2018
Journal International Journal of Computer Applications in Technology
Print ISSN 0952-8091
Publisher Inderscience
Peer Reviewed Peer Reviewed
Volume 56
Issue 4
Pages 253-263
DOI https://doi.org/10.1504/IJCAT.2017.10009946
Keywords micro-Doppler, micro-motion, EEMD, IMF, wheeled/tracked vehicle, SAR/GMTI, signal separation, feature abstraction, vehicle classification, SVM
Publisher URL http://dx.doi.org/10.1504/IJCAT.2017.10009946

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