Volume 12 Issue 2
Apr.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(2): 332-342
TANG Junkui, LIU Zheng, RAN Lei, et al. Radar forward-looking super-resolution imaging method based on sparse and low-rank priors[J]. Journal of Radars, 2023, 12(2): 332–342. doi: 10.12000/JR22199
Citation: TANG Junkui, LIU Zheng, RAN Lei, et al. Radar forward-looking super-resolution imaging method based on sparse and low-rank priors[J]. Journal of Radars, 2023, 12(2): 332–342. doi: 10.12000/JR22199
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Radar Forward-looking Super-resolution Imaging Method Based on Sparse and Low-rank Priors

doi: 10.12000/JR22199

  • National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (62001346), Seed Funding Project of Multisensor Intelligent Detection and Recognition Technologies R&D Center of CASC (ZZJJ202102)
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  • Corresponding author: LIU Zheng, lz@xidian.edu.cn; RAN Lei, rl@xidian.edu.cn
  • Received Date: 2022-09-30
  • Rev Recd Date: 2022-12-24
    • Available Online: 2022-12-27
  • Publish Date: 2022-12-30
    Abstract
  • Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of Compressed Sensing(CS) to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an Augmented Lagrange Multiplier(ALM) within the framework of the Alternating Direction Multiplier Method(ADMM) was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.

     

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    • References(25)
  • [1]
    MAO Deqing, ZHANG Yongchao, PEI Jifang, et al. Forward-looking geometric configuration optimization design for spaceborne-airborne multistatic synthetic aperture radar[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 8033–8047. doi: 10.1109/JSTARS.2021.3103802
    [2]
    陈洪猛, 李明, 王泽玉, 等. 基于多帧数据联合处理的机载单通道雷达贝叶斯前视成像[J]. 电子与信息学报, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153

    CHEN Hongmeng, LI Ming, WANG Zeyu, et al. Bayesian forward-looking imaging for airborne single-channel radar based on combined multiple frames data[J]. Journal of Electronics &Information Technology, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153
    [3]
    RAN Lei, LIU Zheng, LI Tao, et al. An adaptive fast factorized back-projection algorithm with integrated target detection technique for high-resolution and high-squint spotlight SAR imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 171–183. doi: 10.1109/JSTARS.2017.2771503
    [4]
    RAN Lei, LIU Zheng, ZHANG Lei, et al. An autofocus algorithm for estimating residual trajectory deviations in synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3408–3425. doi: 10.1109/TGRS.2017.2670785
    [5]
    梅海文, 孟自强, 李亚超, 等. 双基前视SAR几何定位及同步误差分析[J]. 电子与信息学报, 2018, 40(4): 882–889. doi: 10.11999/JEIT170677

    MEI Haiwen, MENG Ziqiang, LI Yachao, et al. Bistatic forward-looking SAR geometrical positioning and analysis of synchronization error[J]. Journal of Electronics &Information Technology, 2018, 40(4): 882–889. doi: 10.11999/JEIT170677
    [6]
    LIU Zhutian, LI Zhongyu, YU Huaqin, et al. Bistatic forward-looking SAR moving target detection method based on joint clutter cancellation in echo-image domain with three receiving channels[J]. Sensors, 2018, 18(11): 3835. doi: 10.3390/s18113835
    [7]
    温晓杨, 匡纲要, 胡杰民, 等. 基于实波束扫描的相控阵雷达前视成像[J]. 航空学报, 2014, 35(7): 1977–1991. doi: 10.7527/S1000-6893.2013.0545

    WEN Xiaoyang, KUANG Gangyao, HU Jiemin, et al. Forward-looking imaging based on real beam scanning phased array radars[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(7): 1977–1991. doi: 10.7527/S1000-6893.2013.0545
    [8]
    刘向阳, 杨君刚, 孟进, 等. 低信噪比下基于Hough变换的前视阵列SAR稀疏三维成像[J]. 雷达学报, 2017, 6(3): 316–323. doi: 10.12000/JR17011

    LIU Xiangyang, YANG Jungang, MENG Jin, et al. Sparse three-dimensional imaging based on Hough transform for forward-looking array SAR in Low SNR[J]. Journal of Radars, 2017, 6(3): 316–323. doi: 10.12000/JR17011
    [9]
    HUANG Yulin, ZHA Yuebo, WANG Yue, et al. Forward looking radar imaging by truncated singular value decomposition and its application for adverse weather aircraft landing[J]. Sensors, 2015, 15(6): 14397–14414. doi: 10.3390/s150614397
    [10]
    XIA Jie, LU Xinfei, and CHEN Weidong. Multi-channel deconvolution for forward-looking phase array radar imaging[J]. Remote Sensing, 2017, 9(7): 703. doi: 10.3390/rs9070703
    [11]
    ZHA Yuebo, HUANG Yulin, SUN Zhichao, et al. Bayesian deconvolution for angular super-resolution in forward-looking scanning radar[J]. Sensors, 2015, 15(3): 6924–6946. doi: 10.3390/s150306924
    [12]
    李悦丽, 马萌恩, 赵崇辉, 等. 基于单脉冲雷达和差通道多普勒估计的前视成像[J]. 雷达学报, 2021, 10(1): 131–142. doi: 10.12000/JR20111

    LI Yueli, MA Meng’en, ZHAO Chonghui, et al. Forward-looking imaging via Doppler estimates of sum-difference measurements in scanning monopulse radar[J]. Journal of Radars, 2021, 10(1): 131–142. doi: 10.12000/JR20111
    [13]
    GIUSTI E, CATALDO D, BACCI A, et al. ISAR image resolution enhancement: Compressive sensing versus state-of-the-art super-resolution techniques[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(4): 1983–1997. doi: 10.1109/taes.2018.2807283
    [14]
    ZHANG Shunsheng, ZHANG Wei, ZONG Zhulin, et al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(5): 2098–2111. doi: 10.1109/tap.2015.2408337
    [15]
    AO Dongyang, WANG Rui, HU Cheng, et al. A sparse SAR imaging method based on multiple measurement vectors model[J]. Remote Sensing, 2017, 9(3): 297. doi: 10.3390/rs9030297
    [16]
    QIU Wei, ZHOU Jianxiong, and FU Qiang. Jointly using low-rank and sparsity priors for sparse inverse synthetic aperture radar imaging[J]. IEEE Transactions on Image Processing, 2019, 29: 100–115. doi: 10.1109/tip.2019.2927458
    [17]
    ZHANG Xiaohua, BAI Ting, MENG Hongyun, et al. Compressive sensing-based ISAR imaging via the combination of the sparsity and nonlocal total variation[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(5): 990–994. doi: 10.1109/lgrs.2013.2284288
    [18]
    ZENG Chuangzhan, ZHU Weigang, JIA Xin, et al. Sparse aperture ISAR imaging method based on joint constraints of sparsity and low rank[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 168–181. doi: 10.1109/tgrs.2020.2994179
    [19]
    ZHANG Lei, XING Mengdao, QIU Chengwei, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3824–3838. doi: 10.1109/tgrs.2010.2048575
    [20]
    LU Canyi, FENG Jiashi, YAN Shuicheng, et al. A unified alternating direction method of multipliers by majorization minimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 527–541. doi: 10.1109/tpami.2017.2689021
    [21]
    WIPF D P and RAO B D. An empirical bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3704–3716. doi: 10.1109/TSP.2007.894265
    [22]
    ZHANG Zhilin and RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912–926. doi: 10.1109/JSTSP.2011.2159773
    [23]
    COTTER S F, RAO B D, ENGAN K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2477–2488. doi: 10.1109/TSP.2005.849172
    [24]
    WIPF D and NAGARAJAN S. Iterative reweighted ℓ1 and ℓ2 methods for finding sparse solutions[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 317–329. doi: 10.1109/JSTSP.2010.2042413
    [25]
    CAI Jianfeng, CANDÈS E J, and SHEN Zuowei. A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization, 2010, 20(4): 1956–1982. doi: 10.1137/080738970
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