Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

doi:10.23919/JSEE.2024.000073

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (5): 1148-1166.doi: 10.23919/JSEE.2024.000073

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

Ruihui PENG¹^,²(), Xingrui WU²^,*(), Guohong WANG³(), Dianxing SUN¹^,³(), Zhong YANG⁴(), Hongwen LI²()

¹ College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
² Qingdao Innovation and Development Center , Harbin Engineering University, Qingdao 266000, China
³ Information Fusion Research Institute , Naval Aeronautical University, Yantai 264001, China
⁴ National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China

Received:2022-11-24 Online:2024-10-18 Published:2024-11-06
Contact: Xingrui WU E-mail:pengruihui@hrbeu.edu.cn;wxr@hrbeu.edu.cn;wangguohong2008@126.com;sdxdd.hi@163.com;yangzhong_yz@hotmail.com;hongwen@hrbeu.edu.cn
About author:
PENG Ruihui was born in 1979. He received his Ph.D. degree in engineering from Navy University of Engineering, Wuhan, China, in 2010. He is currently a professor of information and communication engineering at Harbin Engineering University. His research interests include radar signal processing, radar target characteristics and radar anti-jamming. E-mail: pengruihui@hrbeu.edu.cn

WU Xingrui was born in 1999. She received her B.S. degree from Yantai University of Technology in 2017. She is currently pursuing her M.S. degree in Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, China. Her research interests include typical jamming environment perception and weak target detection. E-mail: wxr@hrbeu.edu.cn

WANG Guohong was born in 1963. He received his Ph.D. degree from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2002. He is currently a professor with the Information Fusion Research Institute, Navy Aeronautical University. His research interests include hypersonic target tracking, maneuvering target tracking, and multi-radar track-to-track association. E-mail: wangguohong2008@126.com

SUN Dianxing was born in 1983. He received his Ph.D. degree in information and communication engineering from Navy Aeronautical University , Yantai, China, in 2015. He is currently a professor of information and communication engineering at Harbin Engineering University. His research interests include radar signal processing, radar data processing and radar anti-jamming. E-mail: sdxdd.hi@163.com

YANG Zhong was born in 1989. He received his Ph.D. degree from Queen Mary University of London, London, the UK, in 2021. He is a lecturer in Naval University of Engineering. His research interests are machine learning, wireless communication, and radar anti-jamming. E-mail: yangzhong_yz@hotmail.com

LI Hongwen was born in 1999. He is currently pursuing his M.S. degree in Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao, China. His current research interests include motion planning, machine learning and nonlinear control for autonomous multirotor unmanned aeriel vehicles. E-mail: hongwen@hrbeu.edu.cn
Supported by:
This work was supported by Shandong Provincial Natural Science Foundation (ZR2020MF015), and Aerospace Technology Group Stability Support Project (ZY0110020009).

Abstract

Abstract:

In modern war, radar countermeasure is becoming increasingly fierce, and the enemy jamming time and pattern are changing more randomly. It is challenging for the radar to efficiently identify jamming and obtain precise parameter information, particularly in low signal-to-noise ratio (SNR) situations. In this paper, an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue. Firstly, a joint algorithm based on YOLOv5 convolutional neural networks (CNNs) is proposed, which is used to achieve the jamming signal classification and preliminary parameter estimation. Furthermore, an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test, feature region search, position regression, spectrum interpolation, etc., which realizes the accurate estimation of jamming carrier frequency, relative delay, Doppler frequency shift, and other parameters. Finally, the approach has improved performance for complex jamming recognition and parameter estimation under low SNR, and the recognition rate can reach 98% under ?15 dB SNR, according to simulation and real data verification results.

Key words: complex jamming recognition, time frequency feature, convolutional neural network (CNN), parameter estimation

Ruihui PENG, Xingrui WU, Guohong WANG, Dianxing SUN, Zhong YANG, Hongwen LI. Intelligent recognition and information extraction of radar complex jamming based on time-frequency features[J]. Journal of Systems Engineering and Electronics, 2024, 35(5): 1148-1166.

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References 31

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Parameter	YOLOv5s	YOLOv5m	YOLOv5l	YOLOv5x
Depth multiple	0.33	0.67	1.0	1.33
Width multiple	0.50	0.75	1.0	1.25
C3-n(True)	1,3,3	2,6,6	3,9,9	4,12,12
C3-n(False)	1	2	3	4
Parameters(MB)	7.3	21.4	47.0	87.7
Number of convolution	32,64,128,	48,96,192,	64,128,256,	80,160,320,
cores in backbone	256,512	384,768	512,1024	640,1280

Bandwidth/MHz	Carrier frequency/GHz	Sampling frequency/MHz	Pulse width/μs
10	3	20	100

Jamming type	Time delay distribution range /$ \text{μs} $	Frequency distribution range /$ \mathrm{G}\mathrm{H}\mathrm{z} $
Noise AM jamming	0−10	1−2
Dense false target jamming	0−10	2−2.5
Comb spectrum jamming	45−56	3−3.5
Intermittent sampling and forwarding jamming	60−80	0.2−2.8

Parameter	Value
Bandwidth/MHz	3
Number of sampling points	4 184
Pulse width/μs	120
Number of pulses	508

Parameter	Value
Bandwidth/MHz	3
Number of sampling points	4 184
Pulse width/μs	80
Number of pulses	508

Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

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Jamming type	Time delay distribution range/μs
Smart noise jamming	0−140
Slice reconstruction jamming	18−105

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