基于趋势估计的微多普勒分离与特征提取算法

doi:10.12305/j.issn.1001-506X.2021.12.05

Abstract

Abstract:

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.

Key words: micro-motion echo model, curve trend estimation, curve separation, variational mode decomposition (VMD), empirical mode decomposition (EMD)

CLC Number:

TN957

Zhenghong PENG, Degui YANG, Xing WANG, Hao WANG, Zhengliang ZHU. Micro-Doppler separation and feature extraction algorithm based on trend estimation[J]. Systems Engineering and Electronics, 2021, 43(12): 3452-3461.

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References 20

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参数	目标
参数	1	2	3	4
长度/m	2	1.5	4/3	3
底面半径/m	2/3	1	0	1
距离/m	19 300	20 700	18 500	21 300
自旋频率/Hz	1.3	1.5	1.6	1.2
进动频率/Hz	1.3	1.5	1.0	1.4
章动频率/Hz	2.1	2.3	2.6	2.8
进动角/(°)	8	10	9	8
方位角/(°)	20	60	15	60
俯仰角/(°)	40	40	45	50

参数	算法
参数	本文算法	Viterbi	最近邻	峰值法
时间/ms	8.095 2	62.097 0	5.589 3	0.138 5

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

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