基于压缩感知的地基MIMO SAR近场层析成像研究

丁泽刚, 刘旻昆, 王岩, 李根, 凌豪, 李喆, 曾涛, 龙腾

丁泽刚, 刘旻昆, 王岩, 李根, 凌豪, 李喆, 曾涛, 龙腾. 基于压缩感知的地基MIMO SAR近场层析成像研究[J]. 信号处理, 2019, 35(5): 729-740. DOI: 10.16798/j.issn.1003-0530.2019.05.001
引用本文: 丁泽刚, 刘旻昆, 王岩, 李根, 凌豪, 李喆, 曾涛, 龙腾. 基于压缩感知的地基MIMO SAR近场层析成像研究[J]. 信号处理, 2019, 35(5): 729-740. DOI: 10.16798/j.issn.1003-0530.2019.05.001
Ding Zegang, Liu Minkun, Wang Yan, Li Gen, Li Linghao, Li Zhe, Zeng Tao, Long Teng. Near-Field Ground-Based MIMO SAR Tomography via Compressive Sensing[J]. JOURNAL OF SIGNAL PROCESSING, 2019, 35(5): 729-740. DOI: 10.16798/j.issn.1003-0530.2019.05.001
Citation: Ding Zegang, Liu Minkun, Wang Yan, Li Gen, Li Linghao, Li Zhe, Zeng Tao, Long Teng. Near-Field Ground-Based MIMO SAR Tomography via Compressive Sensing[J]. JOURNAL OF SIGNAL PROCESSING, 2019, 35(5): 729-740. DOI: 10.16798/j.issn.1003-0530.2019.05.001

基于压缩感知的地基MIMO SAR近场层析成像研究

基金项目: 国家杰出青年科学基金(61625103);国家自然科学基金重点项目(11833001);国家自然科学基金(31727901)
详细信息
    通讯作者:

    王岩   E-mail: yan_wang@bit.edu.cn

  • 中图分类号: TN959.71

Near-Field Ground-Based MIMO SAR Tomography via Compressive Sensing

More Information
    Corresponding author:

    Wang Yan   E-mail: yan_wang@bit.edu.cn

  • 摘要: 合成孔径雷达(Synthetic Aperture Radar,SAR)层析技术是一种通过多航迹观测,获取目标高程信息,重建目标三维结构的重要手段。目前,层析SAR成像多应用于星载与机载平台,均以远场假设为基础,即认为雷达与目标间的距离远大于目标几何尺寸,此时多航迹观测雷达对目标的视角变化很小,目标的散射特征变化很小。但是在近场构型下,多航迹观测雷达对目标的视角变化大,目标的散射特征变化大,现有基于远场假设的层析处理方法不再适用。为解决这一问题,本文研究了基于压缩感知的地基多输入多输出(Multiple Input Multiple Output, MIMO)SAR近场层析成像方法,主要包含了以下方法创新:(1)基于孤立强点定标完成通道间幅相误差补偿,提高成像质量;(2)基于散射体结构信息与闪烁强点剔除实现高精度多图配准;(3)利用解斜思想与三维空间几何关系估计高程信息。最后,本研究实现了基于Ku频段MIMO SAR的建筑物三维结构反演。
    Abstract: Synthetic Aperture Radar(SAR) tomography is an important technique for target elevation information inversion and reconstruct the 3-D structure of the target via multi-pass observations. At present, SAR tomography is usually implemented based on the far-field assumption where the range between the radar and the target is far larger than the target size. Therefore, the look angle and the target scattering characteristics have good consistency. However, in the case of near-field case where both the look angle and the target scattering characteristics change seriously, the existing SAR tomography technique will fail. In order to solve the problem, this paper proposes a new method to reconstruct the 3-D structure of near-field target via compressive sensing MIMO SAR. The main contribution of this method involves: (1) Using the multi-strong point to compensate the amplitude and phase error between multiple channels to improve images quality; (2) Using scattering structure to extract scattering singular values and improve registration accuracy; (3) Using deramp method and 3-D geometric relationships to get the elevation information. Besides, we have successfully implemented near-field ground-based MIMO SAR tomography via compressive sensing. The presented approach has been evaluated via real Ku-band MIMO SAR data.
  • [1] Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142-2152.
    [2] Gini F, Lombardini F. Multilook APES for multibaseline SAR interferometry[J]. IEEE Transactions on Signal Processing, 2002, 50(7): 1800-1803.
    [3] She Z, Gray D A, Bogner R E, et al. Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing[J]. International Journal of Remote Sensing, 2002, 23(20): 4357-4382.
    [4] Fornaro G, Serafino F, Soldovieri F. Three-dimensional focusing with multipass SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(3): 507-517.
    [5] Fornaro G, Lombardini F, Serafino F. Three-dimensional multipass SAR focusing: Experiments with long-term spaceborne data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 702-714.
    [6] Knaell K K, Cardillo G P. Radar tomography for the generation of three-dimensional images[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142(2): 54-60.
    [7] Ender J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414.
    [8] Zhu X X, Bamler R. Very high resolution spaceborne SAR tomography in urban environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4296-4308.
    [9] Donoho D L. Compressed sensing[J]. IEEE Transactions on information theory, 2006, 52(4): 1289-1306.
    [10] Candès E J. Compressive sampling[C]//Proceedings of the international congress of mathematicians. 2006, 3: 1433-1452.
    [11] Zhu X X, Bamler R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 247-258.
    [12] Shahzad M, Zhu X X. Automatic detection and reconstruction of 2-D/3-D building shapes from spaceborne TomoSAR point clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1292-1310.
    [13] Wang Y, Zhu X X. Automatic feature-based geometric fusion of multi-view TomoSAR point clouds in urban area[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2015, 8(3): 953-965.
    [14] Pottier E, Ferro-Famil L, Fitrzyk M, et al. PolSARpro-Bio: An ESA Educational Toolbox Used For Self-Education in the Field of PolSAR, Pol-InSAR AND Pol-TomoSAR Data Analysis[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018: 6568-6571.
    [15] Pardini M, Papathanassiou K. Beyond TomoSAR vertical reflectivity profiles in forest scenarios: Ground polarimetric covariance estimation at multiple frequencies[C]//Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International. IEEE, 2017: 2464-2467.
    [16] Budillon A, Johnsy A C, Schirinzi G. Sparsity based TomoSAR combining CS and GLRT[C]//EUSAR 2018; 12th European Conference on Synthetic Aperture Radar. VDE, 2018: 1-4.
    [17] Minh D H T, Ngo Y N. Tomosar platform supports for Sentinel-1 tops persistent scatterers interferometry[C]//Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International. IEEE, 2017: 1680-1683.
    [18] Budillon A, Evangelista A, Schirinzi G. Three-dimensional SAR focusing from multipass signals using compressive sampling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 488-499.
    [19] Zhu X X, Bamler R. Tomographic SAR inversion by L1-norm regularization—The compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3839-3846.
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出版历程
  • 收稿日期:  2019-01-23
  • 修回日期:  2019-04-06
  • 发布日期:  2019-05-24

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