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

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (12): 3452-3461.doi: 10.12305/j.issn.1001-506X.2021.12.05

基于趋势估计的微多普勒分离与特征提取算法

彭正红¹, 杨德贵^1,*, 王行², 王浩³, 朱政亮⁴

1. 中南大学航空航天学院, 湖南长沙 410083
2. 中南大学自动化学院, 湖南长沙 410083
3. 浙江大学工业控制技术国家重点实验室, 浙江杭州 310027
4. 厦门大学水声通信与海洋信息技术教育部重点实验室, 福建厦门 361005

收稿日期:2020-09-21 出版日期:2021-11-24 发布日期:2021-11-30
通讯作者: 杨德贵
作者简介:彭正红(1996—), 男, 硕士研究生, 主要研究方向为雷达目标微动特征提取|杨德贵(1978—), 男, 教授, 博士, 主要研究方向为雷达与光学目标特性、雷达电子对抗、雷达信号处理|王行(1996—), 男, 博士研究生, 主要研究方向为雷达目标微动特征|王浩(1998—), 男, 硕士研究生, 主要研究方向为数据驱动知识挖掘、工业大数据|朱政亮(1994—), 男, 博士研究生, 主要研究方向为超宽带雷达目标探测、水声组网通信
基金资助:
装备预研项目(61404130119);中南大学中央高校基本科研业务费专项资金(2020zzts764)

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

Zhenghong PENG¹, Degui YANG^1,*, Xing WANG², Hao WANG³, Zhengliang ZHU⁴

1. School of Aeronautics and Astronautics, Central South University, Changsha 410083, China
2. School of Automation, Central South University, Changsha 410083, China
3. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
4. Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen 361005, China

Received:2020-09-21 Online:2021-11-24 Published:2021-11-30
Contact: Degui YANG

摘要/Abstract

摘要：

微动目标特征提取与辨识一直是弹道目标识别的研究热点与难点。针对复杂运动目标微多普勒(micro-Doppler, m-D)曲线交叠耦合导致的微动辨识难点, 提出一种基于曲线趋势估计的分离算法。该算法首先通过骨架提取获得稳定精细的二值化曲线数据, 再基于曲线光滑性和插值法对曲线趋势进行精确估计并分离, 最后利用变分模态分解(variational mode decomposition, VMD)及经验模态分解(empirical mode decomposition, EMD)算法分解每条m-D曲线并计算相应的微动特性。仿真实验表明, 所提算法能够在信噪比大于-15 dB条件下稳定分离m-D曲线, 进而提取目标的微动特性。

关键词: 微动回波模型, 曲线趋势估计, 曲线分离, 变分模态分解, 经验模态分解

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)

中图分类号:

TN957

彭正红, 杨德贵, 王行, 王浩, 朱政亮. 基于趋势估计的微多普勒分离与特征提取算法[J]. 系统工程与电子技术, 2021, 43(12): 3452-3461.

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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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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基于趋势估计的微多普勒分离与特征提取算法

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

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