Two-dimensional Underwent Synthetic Aperture Radar Imaging Based on Iterative Proximal Projection

LI Jiaqiang; GUO Guixiang; CHEN Jinli; ZHU Yanping

doi:10.11999/JEIT210335

Volume 44 Issue 6

Jun. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 2127-2134

LI Jiaqiang, GUO Guixiang, CHEN Jinli, ZHU Yanping. Two-dimensional Underwent Synthetic Aperture Radar Imaging Based on Iterative Proximal Projection[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2127-2134. doi: 10.11999/JEIT210335

Citation:

LI Jiaqiang, GUO Guixiang, CHEN Jinli, ZHU Yanping. Two-dimensional Underwent Synthetic Aperture Radar Imaging Based on Iterative Proximal Projection[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2127-2134. doi: 10.11999/JEIT210335

Citation:

PDF( 4036 KB)

Two-dimensional Underwent Synthetic Aperture Radar Imaging Based on Iterative Proximal Projection

doi: 10.11999/JEIT210335

LI Jiaqiang^{1, 2
,
,},
GUO Guixiang²,
CHEN Jinli^{1, 2},
ZHU Yanping²

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62071238, 61801231), The Natural Science Foundation of Jiangsu Province (BK20191399)

Received Date: 2021-04-20
Accepted Date: 2022-03-07
Rev Recd Date: 2022-02-28

Available Online: 2022-03-19

Publish Date: 2022-06-21

Abstract

Abstract

Synthetic Aperture Radar (SAR) imaging has a large amount of data volume, high sampling rate, and the problem of SAR imaging precision in traditional Compression Sensing (CS) is low, and there is a problem of poor anti-noise performance. A method of reconstruction method of two - dimensional sampling synthetic aperture rada based on Iterative Proximal Projection (IPP) is proposed. The radar echo is constructed as a two-dimensional sparse representation model in the range frequency-domain-azimuth Doppler region. On this basis, the two-dimensional imaging problem is transformed into the range and azimuth compression sensing sparse reconstruction. The function optimization model of the iterative proximal projection algorithm is used to represent the sparse representation of the synthetic aperture thunder imaging, and the proximal operator is finally obtained with the Smoothly Clipped Absolute Deviation (SCAD) penalty function to solve the model and to image. Simulation and measured data processing results show that the method of imaging is better.

FullText(HTML)

References(18)

References

[1]	ZHU Hongliang, LEUNG R, and HONG Minyi. Shadow compensation for synthetic aperture radar target classification by dual parallel generative adversarial network[J]. IEEE Sensors Letters, 2020, 4(8): 7002904. doi: 10.1109/LSENS.2020.3009179
[2]	XIAO Peng, LIU Bo, and GUO Wei. ConGaLSAR: A constellation of geostationary and low earth orbit synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(12): 2085–2089. doi: 10.1109/LGRS.2019.2962574
[3]	杨磊, 张苏, 黄博, 等. 多任务协同优化学习高分辨SAR稀疏自聚焦成像算法[J]. 电子与信息学报, 2021, 43(9): 2711–2719. doi: 10.11999/JEIT200300 YANG Lei, ZHANG Su, HUANG Bo, et al. Multi-task learning of sparse autofocusing for high-resolution SAR imagery[J]. Journal of Electronics &Information Technology, 2021, 43(9): 2711–2719. doi: 10.11999/JEIT200300
[4]	田鹤, 于海锋, 朱宇, 等. 基于频域稀疏压缩感知的星载SAR稀疏重航过3维成像[J]. 电子与信息学报, 2020, 42(8): 2021–2028. doi: 10.11999/JEJT190638 TIAN He, YU Haifeng, ZHU Yu, et al. Sparse flight 3-D imaging of spaceborne SAR based on frequency domain sparse compressed sensing[J]. Journal of Electronics &Information Technology, 2020, 42(8): 2021–2028. doi: 10.11999/JEJT190638
[5]	BU Hongxia, BAI Xia, and TAO Ran. Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain[J]. Science China Information Sciences, 2012, 55(8): 1789–1800. doi: 10.1007/s11432-012-4607-6
[6]	PATEL V M, EASLEY G R, HEALY D M, et al. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 244–254. doi: 10.1109/JSTSP.2009.2039181
[7]	DONG Xiao and ZHANG Yunhua. A novel compressive sensing algorithm for SAR imaging[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(2): 708–720. doi: 10.1109/JSTARS.2013.2291578
[8]	LIU Zhixue, LI Gang, ZHANG Hao, et al. SAR imaging of dominant scatterers using cascading StOMP[C]. Proceedings of 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 1676–1679.
[9]	BU Hongxia, TAO Ran, BAI Xia, et al. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1003–1007. doi: 10.1109/LGRS.2014.2372319
[10]	徐建平, 皮亦鸣, 曹宗杰. 基于贝叶斯压缩感知的合成孔径雷达高分辨成像[J]. 电子与信息学报, 2011, 33(12): 2863–2868. doi: 10.3724/SP.J.1146.2010.01377 XU Jianping, PI Yiming, and CAO Zongjie. SAR imaging based on Bayesian compressive sensing[J]. Journal of Electronics &Information Technology, 2011, 33(12): 2863–2868. doi: 10.3724/SP.J.1146.2010.01377
[11]	梁美美. 基于压缩感知的SAR成像算法研究[D]. [硕士论文], 哈尔滨工程大学, 2014. LIANG Meimei. Research of SAR imaging arithmetic based on compressed sensing[D]. [Master dissertation], Harbin Engineering University, 2014.
[12]	LI Shiyong, ZHAO Guoqiang, LI Houmin, et al. Near-field radar imaging via compressive sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(2): 828–833. doi: 10.1109/TAP.2014.2381262
[13]	GHAYEM F, SADEGHI M, BABAIE-ZADEH M, et al. Sparse signal recovery using iterative proximal projection[J]. IEEE Transactions on Signal Processing, 2018, 66(4): 879–894. doi: 10.1109/TSP.2017.2778695
[14]	CHEN Jinli, ZHENG Yao, ZHANG Tingxiao, et al. Iterative reweighted proximal projection based DOA estimation algorithm for monostatic MIMO radar[J]. Signal Processing, 2020, 172: 107537. doi: 10.1016/j.sigpro.2020.107537
[15]	FAN Jianqing and LI Runze. Variable selection via Nonconcave penalized likelihood and its oracle properties[J]. Journal of the American Statistical Association, 2001, 96(456): 1348–1360. doi: 10.1198/016214501753382273
[16]	卜红霞, 白霞, 赵娟, 等. 基于压缩感知的矩阵型联合SAR成像与自聚焦算法[J]. 电子学报, 2017, 45(4): 874–881. doi: 10.3969/j.issn.0372-2112.2017.04.016 BU Hongxia, BAI Xia, ZHAO Juan, et al. Joint matrix form SAR imaging and autofocus based on compressed sensing[J]. Acta Electronica Sinica, 2017, 45(4): 874–881. doi: 10.3969/j.issn.0372-2112.2017.04.016
[17]	TSENG P. Convergence of a block coordinate descent method for nondifferentiable minimization[J]. Journal of Optimization Theory and Applications, 2001, 109(3): 475–494. doi: 10.1023/A:1017501703105
[18]	徐楚, 朱栋强, 汪玲, 等. 基于零空间 ${l_1}$ 范数最小化的ISAR成像方法[J]. 系统工程与电子技术, 2020, 42(2): 315–321. doi: 10.3969/j.issn.1001-506X.2020.02.09 XU Chu, ZHU Dongqiang, WANG Ling, et al. ISAR imaging using null space l₁ norm minimization[J]. Systems Engineering and Electronics, 2020, 42(2): 315–321. doi: 10.3969/j.issn.1001-506X.2020.02.09