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

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

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)

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)

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.

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1	GRIFFITHS H D, BAKER C J. An introduction to passive radar. Norwood: Artech House, 2017.
2	KUSCHEL H, CRISTALLINI D, OLSEN K E Tutorial: passive radar tutorial. IEEE Aerospace and Electronic Systems Magazine, 2019, 34 (2): 2- 19.
3	WAN X R, YI J X, ZHAN W J, et al Research progress and development trend of the multi-illuminator-based passive radar. Journal of Radars, 2020, 9 (6): 939- 958.
4	COLONE F, BONGIOANNI C, LOMBATDO P Multifrequency integration in FM radio-based passive bistatic radarPart II: direction of arrival estimation. IEEE Aerospace and Electronic Systems Magazine, 2013, 28 (4): 40- 47. doi: 10.1109/MAES.2013.6506828
5	FILIPPINI F, MARTELLI T, COLONE F, et al Target DOA estimation in passive radar using non-uniform linear arrays and multiple frequency channels. Proc. of the IEEE Radar Conference, 2018, 1290- 1295.
6	GEUN-HO P, DONG-GYU K, JAE K H, et al Maximum-likelihood angle estimator for multi-channel FM-radio-based passive coherent location. IET Radar, Sonar and Navigation, 2018, 12 (6): 617- 625. doi: 10.1049/iet-rsn.2017.0419
7	SU G N, MORF M The signal subspace approach for multiple wide-band emitter location. IEEE Trans. on Acoustics, Speech and Signal Processing, 1983, 31 (6): 1502- 1522. doi: 10.1109/TASSP.1983.1164233
8	WANG H, KAVEH M Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources. IEEE Trans. on Acoustics, Speech and Signal Processing, 1985, 33 (4): 823- 831. doi: 10.1109/TASSP.1985.1164667
9	MALIOUTOV D, CETIN M, WILLSKY A S A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. on Signal Processing, 2005, 53 (8): 3010- 3022. doi: 10.1109/TSP.2005.850882
10	HYDER M M, MAHATA K Direction-of-arrival estimation using a mixed norm approximation. IEEE Trans. on Signal Processing, 2010, 58 (9): 4646- 4655. doi: 10.1109/TSP.2010.2050477
11	CARLIN M, ROCCA G, OLIVERI F V, et al Directions of-arrival estimation through Bayesian compressive sensing strategies. IEEE Trans. on Antennas and Propagation, 2013, 61 (7): 3828- 3838. doi: 10.1109/TAP.2013.2256093
12	DAI J, SO H C Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation. IEEE Trans. on Signal Processing, 2018, 66 (3): 744- 756. doi: 10.1109/TSP.2017.2773420
13	WANG H T, WANG J Super-resolution DOA estimation in passive radar based on compressed sensing. Journal of Electronics and Information Technology, 2013, 35 (4): 877- 881.
14	ZUO L, WANG J, CHEN G Super-resolution DOA estimation method of passive bistatic radar based on TLS-CS. Systems Engineering and Electronics, 2020, 42 (1): 61- 66.
15	WAN L T, KONG X J, XIA F Joint range-Doppler-angle estimation for intelligent tracking of moving aerial targets. IEEE Internet of Things Journal, 2018, 5 (3): 1625- 1636. doi: 10.1109/JIOT.2017.2787785
16	ZHANG X Y, HUO K, LIU Y X, et al Direction of arrival estimation via joint sparse Bayesian learning for bi-static passive radar. IEEE Access, 2019, 7, 72979- 72993. doi: 10.1109/ACCESS.2019.2919069
17	MOSCARDINI C, PETRI D, CAPRIA A, et al Batches algorithm for passive radar: a theoretical analysis. IEEE Trans. on Aerospace and Electronic Systems, 2015, 51 (2): 1475- 1487. doi: 10.1109/TAES.2015.130407
18	YI J X, WAN X R, LI D S, et al Robust clutter rejection in passive radar via generalized subband cancellation. IEEE Trans. on Aerospace and Electronic Systems, 2018, 54 (4): 1931- 1946. doi: 10.1109/TAES.2018.2805228
19	BLAKE S OS-CFAR theory for multiple targets and nonuniform clutter. IEEE Trans. on Aerospace and Electronic Systems, 1988, 24 (6): 785- 790. doi: 10.1109/7.18645
20	WIPF D P, RAO B D An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Trans. on Signal Processing, 2007, 55 (7): 3704- 3716. doi: 10.1109/TSP.2007.894265
21	NANNURU S, GEMBA K L, GERSTOFT P, et al Sparse Bayesian learning with multiple dictionaries. Signal Processing, 2019, 159, 159- 170. doi: 10.1016/j.sigpro.2019.02.003
22	TIPPING M E Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 2001, 1, 211- 244.
23	GERSTOFT P, MECKLENBRAUHER C F, XENAKI A, et al Multisnapshot sparse Bayesian learning for DOA. IEEE Signal Processing Letters, 2016, 23 (10): 1469- 1473. doi: 10.1109/LSP.2016.2598550
24	AUSTIN C D, MOSES R L, ASH J N, et al On the relation between sparse reconstruction and parameter estimation with model order selection. IEEE Journal of Selected Topics in Signal Processing, 2010, 4 (3): 560- 570. doi: 10.1109/JSTSP.2009.2038313
25	LIU Z M, HUANG Z T, ZHOU Y Y An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning. IEEE Trans. on Wireless Communications, 2012, 11 (10): 1- 11. doi: 10.1109/TWC.2012.090312.111912
26	SHEN Q, LIU W, CUI W, et al Focused compressive sensing for underdetermined wideband DOA estimation exploiting high-order difference co-arrays. IEEE Signal Processing Letters, 2016, 24 (1): 86- 90.
27	HUNG H, KAVEH M Focussing matrices for coherent signal-subspace processing. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36 (8): 1272- 1281. doi: 10.1109/29.1655
28	STOICA P, NEHORAI A MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37 (5): 720- 741. doi: 10.1109/29.17564
29	SCHMIDT R Multiple emitter location and signal parameter estimation. IEEE Trans. on Antennas and Propagation, 1986, 34 (3): 276- 280. doi: 10.1109/TAP.1986.1143830
30	XIE D Q, YI J X, SHEN J, et al Experimental research of multi-FM based passive radar. Proc. of the 12th International Symposium on Antennas, Propagation and EM Theory, 2018, 4- 6.

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

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

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