WANG Jun, ZHU He, LEI Peng, ZHENG Tong, GAO Fei. CNN Based Classification of Rigid Targets in Space Using Radar Micro-Doppler Signatures[J]. Chinese Journal of Electronics, 2019, 28(4): 856-862. doi: 10.1049/cje.2018.08.003
Citation: WANG Jun, ZHU He, LEI Peng, ZHENG Tong, GAO Fei. CNN Based Classification of Rigid Targets in Space Using Radar Micro-Doppler Signatures[J]. Chinese Journal of Electronics, 2019, 28(4): 856-862. doi: 10.1049/cje.2018.08.003

CNN Based Classification of Rigid Targets in Space Using Radar Micro-Doppler Signatures

doi: 10.1049/cje.2018.08.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.61501011, No.61671035, No.61771027, No.61071139).
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  • Corresponding author: LEI Peng (corresponding author) received the B.S. and Ph.D. degrees in electrical engineering from Beihang University, Beijing, China, in 2006 and 2012, respectively. He is currently an assistant professor with the School of Electronic and Information Engineering, Beihang University. His research interests include signal processing, especially in time-frequency analysis and spectral estimation, image processing, and target recognition. Dr. Lei was the recipient of the 2011 IEEE IGARSS Student Travel Grant. (Email:peng.lei@buaa.edu.cn)
  • Received Date: 2018-04-12
  • Rev Recd Date: 2018-06-21
  • Publish Date: 2019-07-10
  • Micro-motion characteristics play an important role in some applications of radar target classification. In this paper, a classification method of rigid targets in space using radar micro-Doppler signatures is proposed. Based on the attitude kinematics of rigid targets, we analyze feasibility of classification using micro-Doppler signatures by the relationship among inertial properties of typical rigid targets, their micro-motion characteristics, and corresponding modulation to radar echoes. According to the micro-Doppler time-frequency distribution of echoes and the scale of training sample set, Convolutional neural network (CNN) based feature extraction method and softmax Classifier are designed. Simulations are carried out to validate its effectiveness and discuss the impact of observation duration, composition of training data and size of convolutional kernels on its classification robustness and computational cost.
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