Micro-Doppler separation and feature extraction algorithm based on trend estimation

被引：0
|
作者：
Peng Z. [1 ]
Yang D. [1 ]
Wang X. [2 ]
Wang H. [3 ]
Zhu Z. [4 ]
机构：
[1] School of Aeronautics and Astronautics, Central South University, Changsha
[2] School of Automation, Central South University, Changsha
[3] State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou
[4] Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 12期
关键词：
Curve separation; Curve trend estimation; Empirical mode decomposition (EMD); Micro-motion echo model; Variational mode decomposition (VMD);
D O I：
10.12305/j.issn.1001-506X.2021.12.05
中图分类号：
学科分类号：
摘要：
Feature extraction and identification of micro-motion targets has always been a research difficulty in ballistic target recognition. Aiming at the difficulty of micro-motion identification caused by the overlapping and coupling of micro-Doppler (m-D) curves of complex moving targets, a separation algorithm based on curve trend estimation is proposed. Firstly, the algorithm obtains stable and fine binarization curve data through skeleton extraction. Then, the curve trend is accurately estimated and separated based on curve smoothness and interpolation method. Finally, the variational mode decomposition (VMD) and empirical mode decomposition (EMD) algorithms are used to decompose each m-D curve and calculate the corresponding micro-motion characteristics. Simulation results show that the proposed algorithm can stably separate the m-D curves when the signal to noise ratio is greater than -15 dB, and then extract the micro-motion feature of the target. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：3452 / 3461
页数：9
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