Open AccessCNN Based Classi.cation of Rigid Targets in Space Using Radar Micro‐Doppler Signatures
Author(s) -
Wang Jun,
Zhu He,
Lei Peng,
Zheng Tong,
Gao Fei
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.08.003
Subject(s) - computer science , softmax function , radar , artificial intelligence , doppler radar , convolutional neural network , robustness (evolution) , kinematics , feature extraction , pattern recognition (psychology) , computer vision , physics , telecommunications , biochemistry , chemistry , classical mechanics , gene
Micro‐motion characteristics play an important role in some applications of radar target classi.cation. In this paper, a classi.cation 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 classi.cation 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 Classi.er are designed. Simulations are carried out to validate its e.ectiveness and discuss the impact of observation duration, composition of training data and size of convolutional kernels on its classi.cation robustness and computational cost.