基于改进Dijkstra算法与时频域滤波的雷达目标识别

doi:10.12305/j.issn.1001-506X.2022.10.12

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (10): 3090-3095.doi: 10.12305/j.issn.1001-506X.2022.10.12

基于改进Dijkstra算法与时频域滤波的雷达目标识别

王彩云^1,*, 姚晨¹, 吴钇达¹, 王佳宁², 李晓飞², 黄盼盼¹

1. 南京航空航天大学航天学院, 江苏南京 210016
2. 北京电子工程总体研究所, 北京 100854

收稿日期:2021-06-18 出版日期:2022-09-20 发布日期:2022-10-24
通讯作者: 王彩云
作者简介:王彩云(1975—), 女, 副教授, 博士, 主要研究方向为雷达信号处理、雷达目标检测与识别|姚晨(1997—), 女, 硕士研究生, 主要研究方向为目标识别、弹道导弹识别|吴钇达(1998—), 男, 硕士研究生, 主要研究方向为目标检测与识别|王佳宁(1988—), 女, 副研究员，博士, 主要研究方向为目标识别总体设计|李晓飞(1984—), 女, 研究员，博士, 主要研究方向为目标识别、弹道导弹识别|黄盼盼(1995—), 男, 硕士研究生, 主要研究方向为雷达信号处理、目标识别
基金资助:
国家自然基金(61301211)

Radar target recognition based on improved Dijkstra algorithm with time-frequency domain filtering

Caiyun WANG^1,*, Chen YAO¹, Yida WU¹, Jianing WANG², Xiaofei LI², Panpan HUANG¹

1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Beijing Institute of Electronic System Engineering, Beijing 100854, China

Received:2021-06-18 Online:2022-09-20 Published:2022-10-24
Contact: Caiyun WANG

摘要/Abstract

摘要：

针对弹道中段雷达目标回波的微多普勒特征提取精准度不高导致目标识别率低的问题, 提出一种基于改进Dijkstra算法与时频域滤波相结合的雷达目标分类识别方法。该方法首先采取改进Dijkstra算法提取多分量回波信号中最强分量的瞬时多普勒特征, 然后利用时频域滤波方法滤除最强分量, 依次提取多分量信号的瞬时多普勒特征, 并将该特征应用于弹道中段雷达目标识别。仿真结果表明, 该方法适用于多种微动形式, 提取回波信号的微多普勒特征的精度更高, 对于弹道中段雷达目标平均识别率较高。

关键词: 雷达自动目标识别, 微多普勒, Dijkstra算法, 时频域滤波

Abstract:

An identification method of radar targets based on improved Dijkstra algorithm with time-frequency domain filtering is proposed to solve the problem that the accuracy of micro-Doppler feature extraction of radar targets in the middle of the trajectory is not high, resulting in low target recognition rates. The improved Dijkstra algorithm is used for extracting the instantaneous Doppler features of the strongest component in the multi-component echo signal. Then the strongest component is filtered through the time-frequency domain filter. So the instantaneous Doppler features of the multi-component signal can be extracted in proper sequence, and features are applied to identify radar targets in the middle of the trajectory. The simulation results demonstrate that this method is suitable for a variety of micro-motion forms, the micro-Doppler features of the echo signal have higher accuracy, and the average recognition rate of radar targets in the middle of the trajectory is increased effectively.

Key words: radar automatic target recognition (RATR), micro-Doppler, Dijkstra algorithm, time-frequency domain filtering

中图分类号:

TN957

王彩云, 姚晨, 吴钇达, 王佳宁, 李晓飞, 黄盼盼. 基于改进Dijkstra算法与时频域滤波的雷达目标识别[J]. 系统工程与电子技术, 2022, 44(10): 3090-3095.

Caiyun WANG, Chen YAO, Yida WU, Jianing WANG, Xiaofei LI, Panpan HUANG. Radar target recognition based on improved Dijkstra algorithm with time-frequency domain filtering[J]. Systems Engineering and Electronics, 2022, 44(10): 3090-3095.

图/表 6

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微动形式	φ/(°)	θ/(°)	f_φ/Hz	f_θ/Hz
章动	0~360	5~20	0.3~1.5	0.5~1.5
进动	0~360	5~20	0.3~1.5	-
翻滚	0~360	-	1.5~3.5	0.5~1.5
摆动	0~180	5~20	-	0.5~1.5

微多普勒特征提取算法	识别率				平均识别率
微多普勒特征提取算法	翻滚诱饵	摆动诱饵	章动弹头	进动弹头	平均识别率
Spline插值^[22]	0.845 0	0.835 0	0.775 0	0.875 0	0.832 5
WAFA^[23]	0.835 0	0.800 0	0.895 0	0.785 0	0.828 8
最大峰值算法^[7]	0.845 0	0.825 0	0.865 0	0.790 0	0.831 3
霍夫变换^[8]	0.915 0	0.905 0	0.945 0	0.930 0	0.925 0
维特比算法^[9]	0.940 0	0.920 0	0.950 0	0.935 0	0.936 3
本文算法	0.965 0	0.950 0	0.985 0	0.960 0	0.965 0

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基于改进Dijkstra算法与时频域滤波的雷达目标识别

Radar target recognition based on improved Dijkstra algorithm with time-frequency domain filtering

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