Volume 12 Issue 1
Feb.  2023
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WANG Mou, WEI Shunjun, SHEN Rong, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12(1): 36–52. doi: 10.12000/JR22101
Citation: WANG Mou, WEI Shunjun, SHEN Rong, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12(1): 36–52. doi: 10.12000/JR22101
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3D SAR Imaging Method Based on Learned Sparse Prior

DOI: 10.12000/JR22101

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (61671113, 61501098), The National Key Research and Development Program of China (2017-YFB0502700), The China Scholarship Council (202106070063), The High-Resolution Earth Observation Youth Foundation (GFZX04061502)
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  • Corresponding author: WEI Shunjun, weishunjun@uestc.edu.cn
  • Received Date: 2022-05-24
  • Accepted Date: 2022-07-10
  • Rev Recd Date: 2022-07-09
    • Available Online: 2022-07-12
  • Publish Date: 2022-07-25
    Abstract
  • The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency. Sparse SAR imaging has grown in importance as a research branch in SAR imaging due to the high potential of sparse signal processing techniques based on Compressed Sensing (CS) to show high potential in reducing system complexity and improving imaging quality. However, traditional sparse imaging methods are still constrained by high computational complexity, nontrivial parameter tuning, and poor adaptability to weakly sparse scenes. To address these issues, we propose a new 3D SAR imaging method based on learned sparse priors inspired by the deep unfolding concept. First, the limitations of the matrix-vector linear representation model are discussed, and an imaging operator is introduced to improve the algorithm’s imaging efficiency. Furthermore, this research focuses on algorithm network details, such as network topology design, the problem of complex-valued propagations, optimization constraints of algorithm parameters, and network training details. Finally, through simulations and measured experiments, it is proved that the proposed method can improve the imaging accuracy while reducing the running time by more than one order of magnitude compared with the conventional sparse imaging algorithms.

     

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    • References(29)
  • [1]
    杨建宇. 雷达对地成像技术多向演化趋势与规律分析[J]. 雷达学报, 2019, 8(6): 669–692. doi: 10.12000/JR19099

    YANG Jianyu. Multi-directional evolution trend and law analysis of radar ground imaging technology[J]. Journal of Radars, 2019, 8(6): 669–692. doi: 10.12000/JR19099
    [2]
    吴一戎, 朱敏慧. 合成孔径雷达技术的发展现状与趋势[J]. 遥感技术与应用, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012

    WU Yirong and ZHU Minhui. The developing status and trends of synthetic aperture radar[J]. Remote Sensing Technology and Application, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012
    [3]
    CURLANDER J C and MCDONOUGH R N. Synthetic Aperture Radar[M]. New York: Wiley, 1991.
    [4]
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [5]
    SHI Wuzhen, JIANG Feng, LIU Shaohui, et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 2019, 29: 375–388. doi: 10.1109/TIP.2019.2928136
    [6]
    YANG Xianjun, TAO Xiaofeng, DUTKIEWICZ E, et al. Energy-efficient distributed data storage for wireless sensor networks based on compressed sensing and network coding[J]. IEEE Transactions on Wireless Communications, 2013, 12(10): 5087–5099. doi: 10.1109/TWC.2013.090313.121804
    [7]
    POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006–1020. doi: 10.1109/JPROC.2009.2037526
    [8]
    CAMLICA S, GURBUZ A C, and ARIKAN O. Autofocused spotlight SAR image reconstruction of off-grid sparse scenes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1880–1892. doi: 10.1109/TAES.2017.2675138
    [9]
    PU Wei and WU Junjie. OSRanP: A novel way for radar imaging utilizing joint sparsity and low-rankness[J]. IEEE Transactions on Computational Imaging, 2020, 6: 868–882. doi: 10.1109/TCI.2020.2993170
    [10]
    DONOHO D L, MALEKI A, and MONTANARI A. Message-passing algorithms for compressed sensing[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(45): 18914–18919. doi: 10.1073/pnas.0909892106
    [11]
    BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine Learning, 2011, 3(1): 1–122. doi: 10.1561/2200000016
    [12]
    BECK A and TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183–202. doi: 10.1137/080716542
    [13]
    ZHAO Lifan, WANG Lu, YANG Lei, et al. The race to improve radar imagery: An overview of recent progress in statistical sparsity-based techniques[J]. IEEE Signal Processing Magazine, 2016, 33(6): 85–102. doi: 10.1109/MSP.2016.2573847
    [14]
    FANG Jian, XU Zongben, ZHANG Bingchen, et al. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 352–363. doi: 10.1109/JSTARS.2013.2263309
    [15]
    JIANG Chenglong, ZHANG Bingchen, FANG Jian, et al. Efficient ℓq regularisation algorithm with range–azimuth decoupled for SAR imaging[J]. Electronics Letters, 2014, 50(3): 204–205. doi: 10.1049/el.2013.1989
    [16]
    GREGOR K and LECUN Y. Learning fast approximations of sparse coding[C]. The 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 2010: 399–406.
    [17]
    PU Wei. SAE-Net: A deep neural network for SAR autofocus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5220714. doi: 10.1109/TGRS.2021.3139914
    [18]
    WANG Mou, WEI Shunjun, SHI Jun, et al. CSR-Net: A novel complex-valued network for fast and precise 3-D microwave sparse reconstruction[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4476–4492. doi: 10.1109/JSTARS.2020.3014696
    [19]
    HU Xiaowei, XU Feng, GUO Yiduo, et al. MDLI-Net: Model-driven learning imaging network for high-resolution microwave imaging with large rotating angle and sparse sampling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5212617. doi: 10.1109/TGRS.2021.3110579
    [20]
    WEI Yangkai, LI Yinchuan, DING Zegang, et al. SAR parametric super-resolution image reconstruction methods based on ADMM and deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 10197–10212. doi: 10.1109/TGRS.2021.3052793
    [21]
    WEI Shunjun, LIANG Jiadian, WANG Mou, et al. AF-AMPNet: A deep learning approach for sparse aperture ISAR imaging and autofocusing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5206514. doi: 10.1109/TGRS.2021.3073123
    [22]
    WANG Mou, WEI Shunjun, ZHOU Zichen, et al. Efficient ADMM framework based on functional measurement model for mmW 3-D SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226417. doi: 10.1109/TGRS.2022.3165541
    [23]
    WANG Mou, WEI Shunjun, LIANG Jiadian, et al. TPSSI-Net: Fast and enhanced two-path iterative network for 3D SAR sparse imaging[J]. IEEE Transactions on Image Processing, 2021, 30: 7317–7332. doi: 10.1109/TIP.2021.3104168
    [24]
    PU Wei. Deep SAR imaging and motion compensation[J]. IEEE Transactions on Image Processing, 2021, 30: 2232–2247. doi: 10.1109/TIP.2021.3051484
    [25]
    WANG Mou, WEI Shunjun, LIANG Jiadian, et al. Lightweight FISTA-inspired sparse reconstruction network for mmW 3-D holography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5211620. doi: 10.1109/TGRS.2021.3093307
    [26]
    WANG Mou, WEI Shunjun, LIANG Jiadian, et al. RMIST-Net: Joint range migration and sparse reconstruction network for 3-D mmW imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5205117. doi: 10.1109/TGRS.2021.3068405
    [27]
    ZHANG Jian and GHANEM B. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1828–1837.
    [28]
    ZHOU Yulong, ZHONG Yu, WEI Zhun, et al. An improved deep learning scheme for solving 2-D and 3-D inverse scattering problems[J]. IEEE Transactions on Antennas and Propagation, 2021, 69(5): 2853–2863. doi: 10.1109/TAP.2020.3027898
    [29]
    LOPEZ-SANCHEZ J M and FORTUNY-GUASCH J. 3-D radar imaging using range migration techniques[J]. IEEE Transactions on Antennas and Propagation, 2000, 48(5): 728–737. doi: 10.1109/8.855491
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