基于VMD分解和多域联合分布的雷达辐射源识别

doi:10.12305/j.issn.1001-506X.2023.08.22

Abstract

Abstract:

Aiming at the problems of high computational complexity, insufficient ability to the same type radar and insufficient experiment with actual signal in the existing algorithms, a radar emitter individual identification method based on variational modal decomposition (VMD) and joint distribution feature of time-frequency-energy is proposed. The VMD is carried out on the radar signal, and 13 kinds of characteristic parameters in the time, frequency and time-frequency domain are obtained for the multiple modal components. Finally, the radar individual identification is carried out with the k-nearest neighbor (k-NN) network. The method proposed is verified with actual marine navigation radar signal, and the experimental results show that the method proposed is superior to the two existing typical methods in terms of recognition accuracy and algorithm robustness, and has practical engineering application value.

Key words: radar emitter, emitter identification, variational modal decomposition (VMD), time-frequency analysis

CLC Number:

TN971.1

Lei WANG, Zhiyong ZHANG, Huiqi XU, Weigui ZENG, Silei CAO. Radar emitter identification based on VMD and multi-domain joint distribution[J]. Systems Engineering and Electronics, 2023, 45(8): 2479-2488.

Figures/Tables 13

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特征方案	分解算法	特征值	备注
EMD-EM²	EMD	时频分布熵	F1
		时频一阶矩	F2
		时频二阶矩	F3
VMD-EM²-SF	VMD	时频分布熵	F1
		时频一阶矩	F2
		时频二阶距	F3
		频谱平坦度	F4
		频谱亮度	F5
		频谱偏置度	F6
VMD-TFED	VMD	总能量	F1
		上升时间	F2
		上升斜率	F3
		频域分布熵	F4
		频域分布峰度	F5
		频域分布偏斜度	F6
		频域分布中心	F7
		时域分布熵	F8
		时域分布峰度	F9
		时域分布偏斜度	F10
		时域分布中心	F11
		时频分布熵	F12
		时频分布中心	F13

实验平台	环境/参数	参考值
硬件平台	CPU	AMD R9 5900X
	主频	3.7 GHz
	内存	16 GHz 3 200 MHz
	显卡	RTX2080Ti 11G
软件平台	系统	WIN10 64位专业版
软件平台	软件	Matlab 2020a

算法	平均运行时间
VMD-TFED	3.25
VMD-EM²-SF	2.78
EMD-EM²	16.32

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Radar emitter identification based on VMD and multi-domain joint distribution

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