A spawning particle filter for defocused moving target detection in GNSS-based passive radar

doi:10.23919/JSEE.2023.000033

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1085-1100.doi: 10.23919/JSEE.2023.000033

• Advanced Radar Imaging and Intelligent Processing • Previous Articles Next Articles

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Hongcheng ZENG¹^,²(), Jiadong DENG¹(), Pengbo WANG¹(), Xinkai ZHOU¹(), Wei YANG¹(), Jie CHEN¹^,*()

¹ School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
² National Key Laboratory of Science and Technology on Space Microwave, China Academy of Space Technology, Xi’an 710000, China

Received:2022-02-28 Online:2023-10-18 Published:2023-10-30
Contact: Jie CHEN E-mail:zenghongcheng@buaa.edu.cn;djdong0725@buaa.edu.cn;wangpb7966@buaa.edu.cn;zhoux1nka1@buaa.edu.cn;yangweigigi@sina.com;chenjie@buaa.edu.cn
About author:
ZENG Hongcheng was born in 1989. He received his B.S. degree from China Agriculture University in 2011, Ph.D. degree in signal and information processing from Beihang University, Beijing, China, in 2016. Since 2019, He has been an assistant professor with the School of Electronics and Information Engineering, Beihang University. He was a visiting researcher with the School of Mathematics and Statistics, University of Sheffield, Sheffield, U.K., from 2017 to 2018. He has published more than 20 journal and conference papers. His research interests include high-resolution spaceborne synthetic aperture radar image formation, passive radar signal processing, and moving target detection. E-mail: zenghongcheng@buaa.edu.cn

DENG Jiadong was born in 1995. He received his B.S. degree in information and computational science from Beihang University, Beijing, China, in 2017, where he is currently pursuing his Ph.D. degree in signal and information processing. His research interests include spaceborne synthetic aperture radar (SAR) image formation for terrain observation by progressive scans (TOPS) mode and sliding spotlight mode. E-mail: djdong0725@buaa.edu.cn

WANG Pengbo was born in 1979. He received his Ph.D. degree in information and communication engineering from Beihang University, Beijing, China, in 2007. From 2007 to 2010, he held a postdoctoral position at the School of Electronics and Information Engineering, Beihang University. From 2014 to 2015, he was a visiting researcher with the Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, U.K. Since 2015, he has been an associate professor with the School of Electronics and Information Engineering, Beihang University. His research interests include high-resolution spaceborne synthetic aperture radar (SAR) image formation, novel techniques for spaceborne SAR systems, and multimodal remote sensing data fusion. E-mail: wangpb7966@buaa.edu.cn

ZHOU Xinkai was born in 1992. He received his B.S. degree in electronic engineering from Beihang University, Beijing, China, in 2015, where he is currently pursuing his Ph.D. degree in signal and information processing. His research interests include active/passive synthetic aperture radar (SAR) image formation, Global Navigation Satellite System (GNSS)-based SAR, and GNSS-based moving target detection moving target detection radar. E-mail: zhoux1nka1@buaa.edu.cn

YANG Wei was born in 1983. He received his M.S. and Ph.D. degrees in signal and information processing from Beihang University (BUAA), Beijing, China, in 2008 and 2011, respectively. From 2011 to 2013, he held a post-doctoral position at the School of Electronics and Information Engineering, Beihang University. Since July 2013, he has been with the School of Electronics and Information Engineering, BUAA as a lecturer. From 2016 to 2017, he researched as a visiting researcher with the Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, U.K. He has been an associate professor with the School of Electronics and Information Engineering, BUAA, since 2018. He has authored or co-authored more than 60 journal and conference publications. His research interests include moving target detection, high-resolution spaceborne synthetic aperture radar (SAR) image formation, SAR image quality improvement, and 3D imaging. E-mail: yangweigigi@sina.com

CHEN Jie was born in 1973. He received his B.S. and Ph.D. degrees in information and communication engineering from Beihang University, Beijing, China, in 1996 and 2002, respectively. Since 2004, he has been an associate professor with the School of Electronics and Information Engineering, Beihang University. He was a visiting researcher with the School of Mathematics and Statistics, University of Sheffield, Sheffield, U.K., from 2009 to 2010, working on ionospheric effects on low-frequency space radars that measure forest biomass and ionospheric electron densities. Since July 2011, he has been a professor with the School of Electronics and Information Engineering, Beihang University. His research interests include multimodal remote sensing data fusion, topside ionosphere exploration with spaceborne high frequency/very high frequency-synthetic aperture radar (SAR) systems, and high-resolution spaceborne SAR image formation and SAR image quality enhancement. E-mail: chenjie@buaa.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (62101014) and the National Key Laboratory of Science and Technology on Space Microwave (6142411203307).

Abstract

Abstract:

Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.

Key words: Global Navigation Satellite System (GNSS)-based passive radar (GBPR), defocused target, moving target detection (MTD), likelihood ratio function (LRF), spawning particle filter (SPF)

Hongcheng ZENG, Jiadong DENG, Pengbo WANG, Xinkai ZHOU, Wei YANG, Jie CHEN. A spawning particle filter for defocused moving target detection in GNSS-based passive radar[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1085-1100.

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References 38

14	ZHOU X K, WANG P B, ZENG H C, et al Moving target detection using GNSS-based passive bistatic radar. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5113415.
15	HE X, ZENG T, CHERNIAKOV M Signal detectability in SS-BSAR with GNSS non-cooperative transmitter. IEE Proceedings-Radar Sonar and Navigation, 2005, 152 (3): 124- 132. doi: 10.1049/ip-rsn:20045042
16	TAO R, ZHANG N, WANG Y C Analyzing and compensating the effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar. IET Radar Sonar & Navigation, 2011, 5 (1): 12- 22.
17	XU J, YU J, PENG Y N, et al Radon-Fourier transform for radar target detection, I: generalized Doppler filter bank. IEEE Trans. on Aerospace and Electronic Systems, 2011, 47 (2): 1186- 1202.
18	BELTRAMONTE T, BRACE P, BISCEGLIE M D, et al Simulation-based feasibility analysis of ship detection using GNSS-R delay-Doppler maps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, 1385- 1399. doi: 10.1109/JSTARS.2020.2970221
19	ZHOU X K, WANG P B, CHEN J, et al A modified Radon Fourier transform for GNSS-based bistatic radar target detection. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 3501805.
20	XU J, YU J, PENG Y N, et al Radon-Fourier transform for radar target detection (II): blind speed sidelobe suppression. IEEE Trans. on Aerospace and Electronic Systems, 2011, 47 (4): 2473- 2486. doi: 10.1109/TAES.2011.6034645
21	YU J, XU J, PENG Y N, et al Radon-Fourier transform for radar target detection (III): optimality and fast implementations. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (2): 991- 1004. doi: 10.1109/TAES.2012.6178044
22	CHEN X L, GUAN J, LIU N B, et al Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration. IEEE Trans. on Signal Processing, 2014, 62 (4): 939- 953. doi: 10.1109/TSP.2013.2297682
23	RUTTEN M G, GORDON N J, MASKELL S Recursive track-before-detect with target amplitude fluctuations. IEE Proceedings-Radarm-Sonar and Navigation, 2005, 152, 345- 352. doi: 10.1049/ip-rsn:20045041
24	RISTIC B, ARULAMPALAM S, GORDON N. A beyond the Kalman filter: particle filters for tracking applications. Boston: Artech House, 2004.
25	XU C, HE Z S, LIU H C, et al Bayesian track-before-detect algorithm for nonstationary sea clutter. Journal of Systems Engineering and Electronics, 2021, 32 (6): 1338- 1344. doi: 10.23919/JSEE.2021.000113
26	SALMOND D J, BIRCH H A particle filter for track before-detect. Proc. of the American Control Conference, 2001, 5, 3755- 3760.
27	ROLLASON M, SALMOND D J. A particle filter for track before-detect of a target with unknown amplitude. IEE Target Tracking: Algorithms and Applications, 2001. DOI: 10.1049/ic:20010240.
28	MAHLER R PHD filters of higher order in target number. IEEE Trans. on Aerospace and Electronic Systems, 2007, 43 (4): 1523- 1543. doi: 10.1109/TAES.2007.4441756
29	LI T C, CORCHADO J M, SUN S D, et al Multi-EAP: extended EAP for multi-estimate extraction for SMC-PHD filter. Chinese Journal of Aeronautics, 2017, 30 (1): 369- 379.
30	SI W J, ZHU H F, QU Z Y Multi-sensor Poisson multi-Bernoulli filter based on partitioned measurements. IET Radar, Sonar & Navigation, 2020, 14 (6): 860- 869.
31	SU Z Z, JI H B, ZHANG Y Q A Poisson multi-Bernoulli mixture filter with spawning based on Kullback-Leibler divergence minimization. Chinese Journal of Aeronautics, 2021, 34 (11): 154- 168. doi: 10.1016/j.cja.2020.11.015
32	YANG X J, XING K Y, FENG X L Maneuvering target tracking in dense clutter based on particle filtering. Chinese Journal of Aeronautics, 2011, 24 (2): 171- 180. doi: 10.1016/S1000-9361(11)60021-6
33	UBEDA-MEDINA L, GARCIA-FERNANDEZ A F, GRAJAL J Adaptive auxiliary particle filter for track-before-detect with multiple targets. IEEE Trans. on Aerospace and Electronic Systems, 2017, 53 (5): 2317- 2330. doi: 10.1109/TAES.2017.2691958
34	FREITAS A D, MIHAYLOVA L, GNING A, et al A box particle filter method for tracking multiple extended objects. IEEE Trans. on Aerospace and Electronic Systems, 2019, 55 (4): 1640- 1655. doi: 10.1109/TAES.2018.2874147
35	RUTTEN M, GORDON N, MASKELL S Efficient particle-based track-before-detect in Rayleigh noise. Proc. of the 7th International Conference on Information Fusion, 2004, 693- 700.
36	ZENG H C, CHEN J, WANG P B, et al 2-D coherent integration processing and detecting of aircrafts using GNSS-based passive radar. Remote Sensing, 2018, 10 (7): 1164. doi: 10.3390/rs10071164
37	OLIVARES J, MARTIN P, VALERO E A simple approximation for the modified Bessel function of zero order I₀(x) . Journal of Physics Conference Series, 2018, 1043, 012003. doi: 10.1088/1742-6596/1043/1/012003
1	ZAVOROTNY V U, GLEASON S, CARDELLACH E, et al Tutorial on remote sensing using GNSS bistatic radar of opportunity. IEEE Geoscience and Remote Sensing Magazine, 2014, 24 (4): 8- 45.
2	LI W Q, CARDELLACH E, RIBO S, et al First spaceborne demonstration of BeiDou-3 signals for GNSS reflectometry from CYGNSS constellation. Chinese Journal of Aeronautics, 2021, 34 (9): 1- 10. doi: 10.1016/j.cja.2020.11.016
3	SADEGHI M, BEHNIA F, AMIRI R Maritime target localization from bistatic range measurements in space-based passive radar. IEEE Trans. on Instrumentation and Measurement, 2021, 70, 8502708.
4	ANTONIOU M, CHERNIAKOV M GNSS-based bistatic SAR: a signal processing view. EURASIP Journal on Advances in Signal Processing, 2013, 1, 98.
5	ANTONIOU M, ZENG Z, LI F F, et al Experimental demonstration of passive BSAR imaging using navigation satellites and a fixed receiver. IEEE Geoscience and Remote Sensing Letters, 2012, 9 (3): 477- 481. doi: 10.1109/LGRS.2011.2172571
6	ZHOU X K, CHEN J, WANG P B, et al An efficient imaging algorithm for GNSS-R bi-static SAR. Remote Sensing, 2019, 11 (24): 2945. doi: 10.3390/rs11242945
7	LIU F, FAN X Z, ZHANG T, et al GNSS-based SAR interferometry for 3-D deformation retrieval: algorithms and feasibility study. IEEE Trans. on Geoscience and Remote Sensing, 2018, 56 (10): 5736- 5748.
8	ZHANG Q L, ANTONIOU M, CHANG W G, et al Spatial decorrelation in GNSS-based SAR coherent change detection. IEEE Trans. on Geoscience and Remote Sensing, 2015, 53 (1): 219- 228. doi: 10.1109/TGRS.2014.2321145
9	HE Z Y, YANG Y, CHEN W. A hybrid integration method for moving target detection with GNSS-based passive radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 1184−1193.
10	ZENG H C, CHEN J, WANG P B, et al Moving target detection in multi-static GNSS-based passive radar based on multi-bernoulli filter. Remote Sensing, 2020, 12 (21): 3495. doi: 10.3390/rs12213495
11	MA H, ANTONIOU M, STOVE A G, et al Target kinematic state estimation with passive multistatic radar. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (4): 2121- 2134. doi: 10.1109/TAES.2021.3069283
12	PASTINA D, SANTI F, PIERALICE F, et al Passive radar imaging of ship targets with GNSS signals of opportunity. IEEE Trans. on Geoscience and Remote Sensing, 2021, 59 (3): 2627- 2642. doi: 10.1109/TGRS.2020.3005306
13	MA H, ANTONIOU M, PASTINA D, et al Maritime moving target indication using passive GNSS-based bistatic radar. IEEE Trans. on Aerospace and Electronic Systems, 2018, 54 (1): 115- 130. doi: 10.1109/TAES.2017.2739900
38	GAO H, LI J W Detection and tracking of a moving target using SAR images with the particle filter-based track-before-detect algorithm. Sensors, 2014, 14 (6): 10829- 10845. doi: 10.3390/s140610829

Parameter	Value	Parameter	Value
Wavelength/m	0.255	Supporting length L_f,0/pixel	40
Antenna gain/dB	30	Spawning particles number N_s	400
Target appearance	Frame-3	Continuous/born particles number N_c=N_b	10 000
Target disappearance	Frame-11	Target position/km	(100,100,8)
Threshold P_th	0.5	Target velocity/(m·s⁻¹)	(100,−173,0)
Image frames	13	Image SNR/dB	8, 10
Target RCS/dB	30	SVN #10 position/km	(10 232.9, –15 519.5, 12 420.5)
Sampling rate/MHz	50.0	SVN #10 Velocity/(m·s⁻¹)	(190.2, –2 114.0, –1 798.6)

Parameter	Value
Wavelength/m	0.19
Sampling rate/MHz	62
Signal bandwidth/MHz	2.046
Antenna gain/dB	15
Target speed/( ${\rm{m}}\cdot {\rm{s}}^{-1} $ )	78
SVN #02 position/km	(−21605.1, 14765.4, 5351.2)
SVN #13 position/km	(−13120.6, 20485.8, 10363.6)
SVN #30 position/km	(−23221.1, −1572.9, 13072.6)
Receiver position/km	(−2191.8, 4370.8, 4081.2)
Target position/km	(−2191.3, 4371.9, 4081.6)

Satellite	Doppler/Hz	Range/m
SVN #02	−72.2	1280
SVN #13	−61.9	907
SVN #30	46.3	2149

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

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