期刊文献+

连续场景的稀疏阵列SAR侧视三维成像研究 被引量:11

Research on Continuous Scene Side-looking 3D Imaging Based on Sparse Array
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摘要 合成孔径雷达的回波数据和图像数据都是复数,由于各个分辨单元散射点的初始相位是随机的,致使连续变化地物场景的信号带宽较大,传统的单天线SAR很难实现空间稀疏降采样。该文采用交轨向多天线观测结构,分析了交轨向稀疏阵列SAR的成像模型,首次提出利用信号重构方法,去除散射点随机初相位,降低复信号带宽,以较大间隔的空间稀疏采样实现稀疏阵列SAR侧视3维成像。干涉SAR 2维成像实际数据处理结果验证了通过信号重构可以降低复信号带宽,稀疏阵列SAR侧视3维成像的仿真结果验证了该文方法的有效性。 The raw data of Synthetic Aperture Radar(SAR) is complex value,as well as the image data.Due to the random phase of each scattering cell,continuous scene has a wide signal band which makes single-aperture SAR difficult to realize down-sampling.In this paper,imaging model of cross-track sparse array SAR is analyzed.Signal reconstruction based imaging method under cross-track multi-aperture structure is investigated to eliminate random phases of scatters and reduce the signal bandwidth.Consequently,sparse array side-looking 3D imaging with larger interval space sampling is realized.The results on InSAR 2D real data verify that the bandwidth can be reduced after signal reconstruction.Besides,the simulation experiments on 3D imaging validate the effectiveness of the proposed method.
出处 《电子与信息学报》 EI CSCD 北大核心 2013年第5期1097-1102,共6页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61271422)资助课题
关键词 合成孔径雷达 3维成像 稀疏采样 信号重构 连续场景 SAR 3D imaging Sparse sampling Signal reconstruction Continuous scene
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  • 1Giret R, Jeuland H, and Enert P. A study of 3D-SAR concept for a millimeter wave imaging radar onboard an UAV [C]. European Radar Conference, Amsterdam, 2004: 201-204.
  • 2FGAN.Experimental system ARTINO. http://www.fhr. fgan.de/fhr/fhr_c594_fl_de.html, 2007.
  • 3Du Lei, Wang Yanping, and Hong Wen. Analytic modeling and three-dimensional imaging of downward-looking SAR using bistatic uniform linear array antennas [C]. APSAR, China, 2007: 49-53.
  • 4DLR. SIREV Executive Summary. http://www.dlr.de/ hr/Por taldata/32/Resources/dokumente/SIREV_Executive Summary.pdf, 2007.
  • 5Markus P, Helmut S, and Stephan D. Imaging technologies and applications of microwave radiometry [C]. European Radar Conference, Amsterdam, 2004: 269-273.
  • 6Chen Duofang, Chen Baixiao, and Zhang Shouhong. Muti-input Muti-output radar and sparse array synthetic impulse and aperture Radar [C]. International Conference on Radar, China, 2006: 28-31.
  • 7Li Zhenfang, Bao Zheng, and Wang Hongyang. Performance improvement for constellation SAR using signal processing techniques [J]. IEEE Trans. on AES, 2006, 42(2): 436-452.
  • 8Ruf C S. Numerical annealing of low-redundancy linear arrays [J]. IEEE Trans. on Antennas and Propagation, 1993, 40(1): 85-90
  • 9Soumekh M. Synthetic Aperture Radar Signal Processing with MATLAB Algorithms [M]. New York, Wiley-Interscience, 1999: 195-204.
  • 10KLARE J. A New Airborne Radar for 3D Imaging-Simulation Study of ARTINO [ C ]//6th European Conference on Synthetic Aperture Radar, EUSAR, Dresden, Germany, 2006.

共引文献32

同被引文献67

  • 1杨波.一种设计组合巴克码脉冲压缩旁瓣抑制滤波器的新方法[J].现代雷达,2001,23(5):41-45. 被引量:7
  • 2杨明磊,陈伯孝,张守宏.微波综合脉冲孔径雷达方向图综合研究[J].西安电子科技大学学报,2007,34(5):738-742. 被引量:15
  • 3陈弓,戴晨光,刘航冶.雷达阵地场景三维可视化系统的实现[J].空军雷达学院学报,2007,21(4):248-251. 被引量:4
  • 4Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
  • 5Candes E J, Romberg J, and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
  • 6Kemkemian S and Nouvel-Fiani M. How sparse sampling is useful to radar?[C]. 2013 2nd International Workshop on Compressed Sensing Applied to Radar, Bonn, 2013, A25: 1-7.
  • 7Herman M and Strohmer T. High-resolution radar via compressed sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2275-2284.
  • 8Samadi S, Cetin M, and Masnadi-Shirazi M A. Sparse representation-based synthetic aperture radar imaging[J]. IET Radar, Sonar & Navigation, 2011, 5(2): 182-193.
  • 9Barilone D, Budillon A, and Schirinzi G. Compressive sampling in SAR tomography: results on COSMO-Skymed data[C]. IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012: 475-478.
  • 10Zhu X X and 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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