Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 1044-1055
GAO Zhiqi, SUN Shuchen, HUANG Pingping, et al. Improved L1/2 threshold iterative high resolution SAR imaging algorithm[J]. Journal of Radars, 2023, 12(5): 1044–1055. doi: 10.12000/JR22243
Citation: GAO Zhiqi, SUN Shuchen, HUANG Pingping, et al. Improved L1/2 threshold iterative high resolution SAR imaging algorithm[J]. Journal of Radars, 2023, 12(5): 1044–1055. doi: 10.12000/JR22243
shu

Improved L1/2 Threshold Iterative High Resolution SAR Imaging Algorithm

doi: 10.12000/JR22243
  • 1.

    College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China

  • 2.

    Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China

Funds:  The National Natural Science Foundation of China (61761037, 62071258), The Natural Science Foundation of Inner Mongolia (2021MS06005, 2020ZD18), Basic Scientific Research Business Cost Project of Colleges Directly under the Inner Mongolia (JY20220147)
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  • Corresponding author: HUANG Pingping, hpp@imut.edu.cn
  • Received Date: 2022-12-28
  • Rev Recd Date: 2023-02-05
    • Available Online: 2023-02-08
  • Publish Date: 2023-02-22
    Abstract
  • An improved Synthetic Aperture Radar (SAR) imaging algorithm is proposed to address the issues of low azimuth resolution and noise interference in the sparse sampling condition. Based on the existing L1/2 regularization theory and iterative threshold algorithm, the gradient operator is modified, which can improve the solution accuracy of the reconstructed image and reduce the load of calculation. Then, under full sampling and under-sampling conditions, the original and improved L1/2 iterative threshold algorithm are combined with the approximate observation model to image SAR echo signals and compare their imaging performance. The experimental findings demonstrate that the improved algorithm improves the azimuth resolution of SAR images and has higher convergence performance.

     

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    • References(27)
  • [1]
    GLENTIS G, ZHAO Kexin, JAKOBSSON A, et al. Non-parametric high-resolution SAR imaging[J]. IEEE Transactions on Signal Processing, 2013, 61(7): 1614–1624. doi: 10.1109/TSP.2012.2232662
    [2]
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [3]
    李清泉, 王欢. 基于稀疏表示理论的优化算法综述[J]. 测绘地理信息, 2019, 44(4): 1–9. doi: 10.14188/j.2095-6045.2019015

    LI Qingquan and WANG Huan. Sparse representation based optimization: A survey[J]. Journal of Geomatics, 2019, 44(4): 1–9. doi: 10.14188/j.2095-6045.2019015
    [4]
    NI Jiacheng, ZHANG Qun, LUO Ying, et al. Compressed sensing SAR imaging based on centralized sparse representation[J]. IEEE Sensors Journal, 2018, 18(12): 4920–4932. doi: 10.1109/JSEN.2018.2831921
    [5]
    JUNG D H, KIM H S, KIM C K, et al. Sparse scene recovery for high-resolution automobile FMCW SAR via scaled compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10136–10146. doi: 10.1109/TGRS.2019.2931626
    [6]
    王天云, 刘冰, 魏强, 等. 压缩感知成像雷达研究进展[J]. 电光与控制, 2019, 26(7): 1–8. doi: 10.3969/j.issn.1671-637X.2019.07.001

    WANG Tianyun, LIU Bing, WEI Qiang, et al. A review on research progresses of compressed sensing imaging radar[J]. Electronics Optics &Control, 2019, 26(7): 1–8. doi: 10.3969/j.issn.1671-637X.2019.07.001
    [7]
    JIANG Hai, JIANG Chenglong, ZHANG Bingchen, et al. Experimental results of spaceborne stripmap SAR raw data imaging via compressed sensing[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 202–205.
    [8]
    ALONSO M T, LOPEZ-DEKKER M, and MALLORQUI J J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4285–4295. doi: 10.1109/TGRS.2010.2051231
    [9]
    ZENG Jinshan, FANG Jian, and XU Zongben. Sparse SAR imaging based on L1/2 regularization[J]. Science China Information Sciences, 2012, 55(8): 1755–1775. doi: 10.1007/s11432-012-4632-5
    [10]
    史洪印, 贾宝京, 齐兆龙. 基于压缩感知的非均匀脉冲SAR欺骗性干扰抑制方法[J]. 仪器仪表学报, 2016, 37(3): 525–532. doi: 10.3969/j.issn.0254-3087.2016.03.007

    SHI Hongyin, JIA Baojing, and QI Zhaolong. Novel non-uniform pulse SAR deception jamming suppressing method based on compressive sensing[J]. Chinese Journal of Scientific Instrument, 2016, 37(3): 525–532. doi: 10.3969/j.issn.0254-3087.2016.03.007
    [11]
    段化军, 朱岱寅, 李勇, 等. 基于压缩感知的条带SAR缺失数据恢复成像方法[J]. 系统工程与电子技术, 2016, 38(5): 1025–1031. doi: 10.3969/j.issn.1001-506X.2016.05.09

    DUAN Huajun, ZHU Daiyin, LI Yong, et al. Recovery and imaging method for missing data of the strip-map SAR based on compressive sensing[J]. Systems Engineering and Electronics, 2016, 38(5): 1025–1031. doi: 10.3969/j.issn.1001-506X.2016.05.09
    [12]
    李博, 刘发林, 周崇彬, 等. 基于近似观测的加权L1压缩感知SAR成像[J]. 微波学报, 2018, 34(6): 62–67. doi: 10.14183/j.cnki.1005-6122.201806014

    LI Bo, LIU Falin, ZHOU Chongbin, et al. Approximated observation-based weighted L1 compressed sensing SAR imaging[J]. Journal of Microwaves, 2018, 34(6): 62–67. doi: 10.14183/j.cnki.1005-6122.201806014
    [13]
    徐宗本, 吴一戎, 张冰尘, 等. 基于L1/2正则化理论的稀疏雷达成像[J]. 科学通报, 2018, 63(14): 1306–1319. doi: 10.1360/N972018-00372

    XU Zongben, WU Yirong, ZHANG Bingchen, et al. Sparse radar imaging based on L1/2 regularization theory[J]. Chinese Science Bulletin, 2018, 63(14): 1306–1319. doi: 10.1360/N972018-00372
    [14]
    杨卫星, 朱岱寅. 稀疏场景下SAR方位向随机丢失数据的迭代成像算法[J]. 系统工程与电子技术, 2021, 43(7): 1748–1755. doi: 10.12305/j.issn.1001-506X.2021.07.03

    YANG Weixing and ZHU Daiyin. Iterative imaging algorithm for SAR azimuth random missing data with sparse scenes[J]. Systems Engineering and Electronics, 2021, 43(7): 1748–1755. doi: 10.12305/j.issn.1001-506X.2021.07.03
    [15]
    ZHANG Jian and GHANEM B. ISTA-net: Interpretable optimization-inspired deep network for image compressive sensing[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1828–1837.
    [16]
    赵克祥, 毕辉, 张冰尘. 基于快速阈值迭代的SAR层析成像处理方法[J]. 系统工程与电子技术, 2017, 39(5): 1019–1023. doi: 10.3969/j.issn.1001-506X.2017.05.11

    ZHAO Kexiang, BI Hui, and ZHANG Bingchen. SAR tomography method based on fast threshold iteration iterative shrinkage-thresholding[J]. Systems Engineering and Electronics, 2017, 39(5): 1019–1023. doi: 10.3969/j.issn.1001-506X.2017.05.11
    [17]
    BI Hui and BI Guoan. Performance analysis of iterative soft thresholding algorithm for L1 regularization based sparse SAR imaging[C]. 2019 IEEE Radar Conference, Boston, USA, 2019: 1–6.
    [18]
    XU Zhongming, WANG Qinghua, HE Yansong, et al. A monotonic two-step iterative shrinkage/thresholding algorithm for sound source identification based on equivalent source method[J]. Applied Acoustics, 2018, 129: 386–396. doi: 10.1016/j.apacoust.2017.07.012
    [19]
    NESTEROV Y E. A method for solving the convex programming problem with convergence rate O (1/k2)[J]. Doklady Akademii Nauk SSSR, 1983, 269(3): 543–547.
    [20]
    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
    [21]
    张倩, 李海洋. 一种改进的迭代软阈值算法及其应用[J]. 纺织高校基础科学学报, 2018, 31(2): 253–260. doi: 10.13338/j.issn.1006-8341.2018.02.020

    ZHANG Qian and LI Haiyang. An improved iterative soft thresholding algorithm and application[J]. Basic Sciences Journal of Textile Universities, 2018, 31(2): 253–260. doi: 10.13338/j.issn.1006-8341.2018.02.020
    [22]
    XU Zongben. Data modeling: Visual psychology approach and L1/2 regularization theory[C]. International Congress of Mathematicians 2010 (ICM 2010), Hyderabad, India, 2010: 3151–3184.
    [23]
    XU Zongben, GUO Hailiang, WANG Yao, et al. Representative of L1/2 regularization among Lq ( 0<q≤ 1) regularizations: An experimental study based on phase diagram[J]. Acta Automatica Sinica, 2012, 38(7): 1225–1228. doi: 10.1016/S1874-1029(11)60293-0
    [24]
    XU Zongben, CHANG Xiangyu, XU Fengmin, et al. L1/2 regularization: A thresholding representation theory and a fast solver[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(7): 1013–1027. doi: 10.1109/TNNLS.2012.2197412
    [25]
    ZENG Jinshan, LIN Shaobo, WANG Yao, et al. L1/2 regularization: Convergence of iterative half thresholding algorithm[J]. IEEE Transactions on Signal Processing, 2014, 62(9): 2317–2329. doi: 10.1109/TSP.2014.2309076
    [26]
    BI Hui, ZHU Daiyin, BI Guoan, et al. FMCW SAR sparse imaging based on approximated observation: An overview on current technologies[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4825–4835. doi: 10.1109/JSTARS.2020.3017487
    [27]
    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
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