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

收稿日期:2018-08-22 出版日期:2019-12-20 发布日期:2019-12-25

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

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1096-1109.

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.

图/表 19

参考文献 28

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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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