Near-field 3D imaging approach combining MJSR and FGG-NUFFT

doi:10.21629/JSEE.2019.06.06

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (6): 1096-1109.doi: 10.21629/JSEE.2019.06.06

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Near-field 3D imaging approach combining MJSR and FGG-NUFFT

Shuzhen WANG¹(), Yang FANG²(), Jin'gang ZHANG^3,^4,*(), Mingshi LUO⁵(), Qing LI⁶()

¹ School of Computer Science and Technology, Xidian University, Xi'an 710071, China
² School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
³ Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
⁴ College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
⁵ College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
⁶ The Fourth Research Institute of Telecommunications Technology Co., Ltd, Xi'an 710061, China

Received:2018-08-22 Online:2019-12-20 Published:2019-12-25
Contact: Jin'gang ZHANG E-mail:shuzhenwang@xidian.edu.cn;fang_yang122@vip.sina.com;zhangjg@ucas.ac.cn;luomsh@xsyu.edu.cn;liqing@telfri.net
About author:WANG Shuzhen was born in 1978. He received his Ph.D. degree from School of Electro-Mechanical Engineering, Xidian University in 2005. He is currently a professor in the School of Computer Science and Technology, Xidian University. His research interests include radar imaging, machine learning, and computer vision. E-mail: shuzhenwang@xidian.edu.cn|FANG Yang was born in 1988. He received his M.S. degree from School of Electronics and Information, Northwestern Polytechnical University (NWPU). He is currently a Ph.D. student in department of Electronics and Information, NWPU. His research interests include radar imaging, imaging processing and telemetry antenna. E-mail: fang_yang122@vip.sina.com|ZHANG Jin'gang was born in 1988. He is an associate professor at the University of Chinese Academy of Sciences. His research interests include image denosing, deblurring and dehazing, image/video analysis and enhancement, and related high-level vision problems. E-mail: zhangjg@ucas.ac.cn|LUO Mingshi was born in 1966. He received his B.S. degree in Beijing University of Posts and Telecommunications in 1988, and M.S. degree in Xi'dian University in 2003. He is now an associate professor at the School of Computer Science, Xi'an Shiyou University. His main research interests include image processing and signal processing. E-mail: luomsh@xsyu.edu.cn|LI Qing was born in 1976. She received her M.S. degree from School of Computer, Xidian University. Currently, she works in the Fourth Institute of Telecommunications Science and Technology Co., Ltd. Her research interest include wireless communication, information security algorithms, radar image, and telemetry antenna. E-mail: liqing@telfri.net
Supported by:
the National Natural Science Foundation of China(61771369);the National Natural Science Foundation of China(61775219);the National Natural Science Foundation of China(61640422);the Fundamental Research Funds for the Central Universities(JB180310);the Equipment Research Program of the Chinese Academy of Sciences(YJKYYQ20180039);the Shaanxi Provincial Key R & D Program(2018SF-409);the Shaanxi Provincial Key R & D Program(2018ZDXM-SF-027);the Natural Science Basic Research Plan;This work was supported by the National Natural Science Foundation of China (61771369; 61775219; 61640422), the Fundamental Research Funds for the Central Universities (JB180310), the Equipment Research Program of the Chinese Academy of Sciences (YJKYYQ20180039), the Shaanxi Provincial Key R & D Program (2018SF-409; 2018ZDXM-SF-027), and the Natural Science Basic Research Plan

Abstract

Abstract:

A near-field three-dimensional (3D) imaging method combining multichannel joint sparse recovery (MJSR) and fast Gaussian gridding nonuniform fast Fourier transform (FGG-NUFFT) is proposed, based on a perfect combination of the compressed sensing (CS) theory and the matched filtering (MF) technique. The approach has the advantages of high precision and high efficiency: multichannel joint sparse constraint is adopted to improve the problem that the images recovered by the single channel imaging algorithms do not necessarily share the same positions of the scattering centers; the CS dictionary is constructed by combining MF and FGG-NUFFT, so as to improve the imaging efficiency and memory requirement. Firstly, a near-field 3D imaging model of joint sparse recovery is constructed by combining the MF-based imaging method. Secondly, FGG-NUFFT and reverse FGG-NUFFT are used to replace the interpolation and Fourier transform in MF-based imaging methods, and a sensing matrix with high precision and high efficiency is constructed according to the traditional imaging process. Thirdly, a fast imaging recovery is performed by using the improved separable surrogate functionals (SSF) optimization algorithm, only with matrix and vector multiplication. Finally, a 3D imagery of the near-field target is obtained by using both the horizontal and the pitching interferometric phase information. This paper contains two imaging models, the only difference is the sub-aperture method used in inverse synthetic aperture radar (ISAR) imaging. Compared to traditional CS-based imaging methods, the proposed method includes both forward transform and inverse transform in each iteration, which improves the quality of reconstruction. The experimental results show that, the proposed method improves the imaging accuracy by about $\pmb{O(10)}$, accelerates the imaging speed by five times and reduces the memory usage by about $\pmb{O(10^2)}$.

Key words: interference imaging, joint sparse recovery, compressed sensing (CS), matching filtering (MF), fast Gaussian gridding, nonuniform fast Fourier transform (NUFFT), near-field 3D imaging

Shuzhen WANG, Yang FANG, Jin'gang ZHANG, Mingshi LUO, Qing LI. Near-field 3D imaging approach combining MJSR and FGG-NUFFT[J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1096-1109.

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References 28

1	FANG Y, WANG B P, SUN C, et al. Near field 3-D imaging approach for joint high-resolution imaging and phase error correction. Journal of Systems Engineering and Electronics, 2017, 28 (2): 199- 211. doi: 10.21629/JSEE.2017.02.01
2	YANG X H, ZHENG Y R, GHASR T, et al. Microwave imaging from sparse measurements for near-field synthetic aperture radar. IEEE Trans. on Instrumentation & Measurement, 2017, 66 (10): 2680- 2692.
3	SHEEN D M, MCMARKIN D L. Three-dimensional radar imaging techniques and systems for near-field applications. Proc. of SPIE, Radar Sensor Technology XX, 2016, 98290V.
4	ZHANG Y, DENG B, YANG Q, et al. Near-field three-dimensional planar millimeter-wave holographic imaging by using frequency scaling algorithm. Sensors, 2017, 17 (10): 1- 14. doi: 10.1109/JSEN.2017.2685484
5	SHEEN D M, MCMARKIN D L, HALL T E. Near-field three-dimensional radar imaging techniques and applications. Applied Optics, 2010, 49 (19): 83- 93. doi: 10.1364/AO.49.000E83
6	LI S Y, ZHU B C, SUN H J. NUFFT-based near-field imaging technique for far-field radar cross section calculation. IEEE Antennas & Wireless Propagation Letters, 2010, 9 (1): 550- 553.
7	ZHU R Q, ZHOU J X, JIANG G, et al. Range migration algorithm for near-field MIMO-SAR imaging. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (12): 2280- 2284. doi: 10.1109/LGRS.2017.2761838
8	FORTUNY J. Efficient algorithms for three-dimensional near-field synthetic aperture radar imaging. Karlsruher, Germany: University of Karslruhe, 2001.
9	DEMIRCI S, CETINKAYA H, TEKBAS M, et al. Back-projection algorithm for ISAR imaging of near-field concealed objects. Proc. of the URSI General Assembly and Scientific Symposium, 2011, 1- 4.
10	LI X, BOND E J, VAN VEEN B D. An overview of ultra-band microwave imaging via space-time beamforming for early-stage breast-cancer detection. IEEE Antennas and Propagation Magazine, 2005, 47 (1): 19- 34. doi: 10.1109/MAP.2005.1436217
11	KAN Y Z, ZHU Y F, TANG L, et al. FGG-NUFFT-based method for near-field 3-D imaging using millimeter waves. Sensors, 2016, 16 (9): 1- 15. doi: 10.1109/JSEN.2016.2532218
12	KAJBAF H, CASE J T, YANG Z L, et al. Compressed sensing for SAR-based wideband three-dimensional microwave imaging system using non-uniform fast Fourier Trans.orm. IET Radar, Sonar and Navigation, 2013, 7 (6): 658- 670. doi: 10.1049/iet-rsn.2012.0149
13	LIU Y B, LI N, WANG R, et al. Achieving high-quality three-dimensional InISAR imaging of maneuvering via super-resolution ISAR imaging by exploiting sparseness. IEEE Geoscience and Remote Sensing Letters, 2014, 11 (4): 828- 832. doi: 10.1109/LGRS.2013.2279402
14	ZHANG Q, YEO T S, DU G, et al. Estimation of three-dimensional motion parameters in interferometric ISAR imaging. IEEE Geoscience and Remote Sensing Letters, 2004, 42 (2): 292- 300. doi: 10.1109/TGRS.2003.815669
15	DONOHO D L. Compressed sensing. IEEE Trans. on Information Theory, 2006, 52 (4): 1289- 1306. doi: 10.1109/TIT.2006.871582
16	CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Information Theory, 2006, 52 (2): 489- 509.
17	BARANIUK R, STEEGHS P. Compressive radar imaging. Proc. of the IEEE Radar Conference, 2007, 128- 133.
18	LI S Y, ZHAO G Q, LI H M, et al. Near-field radar imaging via compressive sensing. IEEE Trans. on Antennas and Propagation, 2015, 63 (2): 828- 833. doi: 10.1109/TAP.2014.2381262
19	AUSTIN C D, ERTIN E, MOSES R L. Sparse signal methods for 3-D radar imaging. IEEE Journal of Selected Topics in Signal Processing, 2011, 5 (3): 408- 423. doi: 10.1109/JSTSP.2010.2090128
20	YAIR R, ADRIAN S. Compressed imaging with a separable sensing operator. IEEE Signal Processing Letters, 2009, 16 (6): 449- 452. doi: 10.1109/LSP.2009.2017817
21	YANG J G, THOMPSON J, HUANG X T, et al. Segmented reconstruction for compressed sensing SAR imaging. IEEE Trans. on Geoscience Remote Sensing, 2013, 51 (7): 4214- 4225. doi: 10.1109/TGRS.2012.2227060
22	LI S D, CHEN W F, YANG J, et al. A fast complex linearized Bregman iteration algorithm and its application in ISAR imaging. Scientia Sinica Informationis, 2015, 45 (9): 1179- 1196. doi: 10.1360/N112014-00316
23	SUN S L, ZHU G F, JIN T. Novel methods to accelerate CS radar imaging by NUFFT. IEEE Trans. on Geoscience and Remote Sensing, 2015, 53 (1): 557- 566. doi: 10.1109/TGRS.2014.2325492
24	FANG J, XU Z B, ZHANG B C, et al. Fast compressed sensing SAR imaging based on approximated observation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (1): 352- 363. doi: 10.1109/JSTARS.2013.2263309
25	SHEEN D M, MCMARKIN D L, HALL T E. Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Trans. on Microwave Theory and Techniques, 2001, 49 (9): 1581- 1592. doi: 10.1109/22.942570
26	DUTT A, ROKHLIN V. Fast Fourier Trans.orms for nonequispaced data. SIAM Journal on Scientific Computing, 1993, 14 (6): 1368- 1393. doi: 10.1137/0914081
27	LEE G J Y. Accelerating the nonuniform fast Fourier Trans.orm. SIAM Review, 2004, 46 (3): 443- 454. doi: 10.1137/S003614450343200X
28	CANDÈS E J. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathématique, 2008, 364(9-10): 589-592. (in French)

Parameter	Value
Center frequency/GHz	10
Bandwidth/GHz	6
Sampling number of frequency	512
Azimuth accumulation angle/($^{\circ}$)	51
Sampling number of direction	71$*$17
Pitch angle/($^\circ$)	0.07

Parameter	Value
Center frequency/GHz	10
Bandwidth/GHz	4
Interval of frequency sampling/(MHz$*$M)	40 (Sparse sampling)
Length of aperture/m	1
Antenna scanning interval/(m$*$N)	0.02 (Sparse sampling)
Baseline length/m	0.02
Distance between antenna and target/m	2

Approach	Target 1 $(x, y, z)$	Target 2 $(x, y, z)$	Target 3 $(x, y, z)$
Original coordinate	(0.012 5, 0.268 8, 0)	(0.143 8, 0.243 8, 0)	(0.118 8, 0.168 8, 0.046 7)
Traditional method	(0, 0.25, 0.65)	(0.119, 0.229, 0.15)	(0.12, 0.11, 0.15)
Proposed method	(0.012, 0.266, 0)	(0.141, 0.241, 0.02)	(0.117 3, 0.16, 0.046)

Approach	Target 1	Target 2	Target 3	Target 4	Target 5
Theoretical coordinate	(-0.2, -0.2, -0.2)	(0.2, -0.2, -0.2)	(0, 0, 0)	(-0.2, 0.2, 0.2)	(0.2, 0.2, 0.2)
Proposed method	(-0.2, -0.2, -0.21)	(0.21, -0.2. -0.2)	(0, 0, 0)	(-0.2, 0.2, 0.2)	(0.2, 0.2, 0.19)
Traditional method	(-0.19, -0.2, 0.2)	(0.2, -0.18, -0.2)	(0.03, -0.01, 0.1)	(-0.2, 0.2, 0.19)	(0.25, 0.19, -0.2)
Traditional method	(-0.19, -0.21, -0.2)	—	(0.03, 0.01, -0.1)	—	(0.25, 0.21, 0.2)

Method	Imaging time/s
Traditional CS imaging method	12.805
Proposed method	2.02

Near-field 3D imaging approach combining MJSR and FGG-NUFFT

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