DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar

doi:10.23919/JSEE.2022.000103

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1052-1063.doi: 10.23919/JSEE.2022.000103

收稿日期:2021-10-21 接受日期:2022-06-10 出版日期:2022-10-27 发布日期:2022-10-27

DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar

Jinfang WEN(), Jianxin YI*(), Xianrong WAN(), Ziping GONG(), Ji SHEN()

¹ School of Electronic Information, Wuhan University, Wuhan 430072, China

Received:2021-10-21 Accepted:2022-06-10 Online:2022-10-27 Published:2022-10-27
Contact: Jianxin YI E-mail:wenjinfang@whu.edu.cn;jxyi@whu.edu.cn;xrwan@whu.edu.cn;zpgong@whu.edu.cn;jishen@whu.edu.cn
About author:|WEN Jinfang was born in 1985. She received her M.S. degree in electromagnatic field and microwave technology from Guilin University of Electronic Technology, in 2012. She is currently working toward her Ph.D. degree at the School of Electronic Information, Wuhan University. Her research interests are passive radar signal processing and array signal processing. E-mail: wenjinfang@whu.edu.cn||YI Jianxin was born in 1989. He received his B.E. degree in electrical and electronic engineering and Ph.D. degree in radio physics from Wuhan University, China, in 2011 and 2016, respectively. From August 2014 to August 2015, he was a visiting Ph.D. student at the University of Calgary, Calgary, AB, Canada. He is currently an associate professor with the School of Electronic Information, Wuhan University. His main research interests include radar signal processing, target tracking, and information fusion. E-mail: jxyi@whu.edu.cn||WAN Xianrong was born in 1975. He received his B.E. degree in electrical and electronic engineering from the former Wuhan Technical University of Surveying and Mapping, Wuhan, China, in 1997, and Ph.D. degree in radio physics from Wuhan University, Wuhan, in 2005. He is currently a professor and a Ph.D. candidate supervisor with the School of Electronic Information, Wuhan University. In recent years, he has hosted and participated in more than 10 national research projects and published more than 80 academic papers. His main research interests include the design of new radar system such as passive radar, over-the-horizon radar, and array signal processing.E-mail: xrwan@whu.edu.cn||GONG Ziping was born in 1977. He received his B.E. degree and Ph.D. degree in radio physics from Wuhan University, in 1999 and 2007, respectively. He is currently a lecturer with the School of Electronic Information, Wuhan University. His research interests include electromagnetic wave propagation and radio ocean remote sensing.E-mail: zpgong@whu.edu.cn||SHEN Ji was born in 1994. He received his B.E. degree in electronic science and technology from Hefei University of Technology, in 2016. He is currently working toward his Ph.D. degree at the School of Electronic Information, Wuhan University. His research interests are passive radar signal processing and array signal processing.E-mail: jishen@whu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (62071335; 61931015; 61831009) and the Technological Innovation Project of Hubei Province of China (2019AAA061)

摘要/Abstract

Abstract:

This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival (DOA) estimation algorithm to improve estimation accuracy and resolution. The developed algorithm exploits the sparsity of targets in the spatial domain. Specifically, we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression, coherent integration, beamforming, and constant false alarm rate (CFAR) detection. Then, based on the framework of sparse Bayesian learning, the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization. Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms, especially under the scenarios of low signal-to-noise ratio (SNR) and small snapshots. Furthermore, the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.

Key words: multi-frequency passive radar, DOA estimation, sparse Bayesian learning, small snapshot, low signal-to-noise ratio (SNR)

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1052-1063.

Jinfang WEN, Jianxin YI, Xianrong WAN, Ziping GONG, Ji SHEN. DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1052-1063.

图/表 11

参考文献 30

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Parameter	Multipath clutter	Target 1	Target 2	Target 3
Bistatic range/km	0, 10, 20, 30, 40, 50, 90, 150,180	100	45	82
Doppler/Hz	0	50	?30	?90
DOA/(°)	0, 5, ?4, 18, ?20, 30, ?25, 35, 40	0	5	50
(CNR/SNR)/dB	63, 52, 43, 31, 25, 15, 5, 0, ?5	?30	?30	?30

RMSE	MAPSBL (97 MHz)	MAPSBL (102.6 MHz)	MFJSBL	AMAPSBL
Target 1	3.04	3.10	2.36	2.52
Target 2	3.61	2.73	2.23	3.17

DOA estimation based on multi-frequency joint sparse Bayesian learning for passive radar

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