Volume 5 Issue 1
Feb.  2016
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Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135
Citation: Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135

Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase

doi: 10.12000/JR15135
Funds:

The National Natural Science Foundation of China (61201390), The Key Scientific Research Project in Universities of Henan Province (16A510004), The Plan for Young Backbone Teacher of Henan Province (2015GGJS038)

  • Received Date: 2015-12-31
  • Rev Recd Date: 2016-01-24
  • Publish Date: 2016-02-28
  • Observation data obtained from the Four-Dimensional (4D) Synthetic Aperture Radar (SAR) system is sparse and non-uniform in the baseline-time plane. Hence, the imaging results acquired by traditional Fourier-based methods are limited by high side lobes. Compressive Sensing (CS) is a recently proposed technique that allows for the recovery of an unknown sparse signal with overwhelming probability from very limited samples. However, the standard CS framework has been developed for real-valued signals, and the imaging process for 4D synthetic aperture radar deals with complex-valued data. In this study, we propose a new 4D synthetic aperture radar imaging algorithm based on an iterative reconstruction of magnitude and phase, which transforms the height-velocity imaging problem of 4D synthetic aperture radar into a joint reconstruction problem of the magnitude and phase of the complex-valued scatter coefficient. Using the phase information in the algorithm, the image quality is improved. Simulation results confirm the effectiveness of the proposed method.

     

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  • [1]
    Morrison K, Bennett J C, and Nolan M. Using DInSAR to separate surface and subsurface features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3424-3430.
    [2]
    Fornaro G, D'Agostino N, Giuliani R, et al.. Assimilation of GPS-derived atmospheric propagation delay in DInSAR data processing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(2): 784-799.
    [3]
    Fornaro G, Reale D, and Serafino F. Four-dimensional SAR imaging for height estimation and monitoring of signal and double scatterers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 224-237.
    [4]
    Lombardini F. Differential tomography: a new framework for SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(1): 37-44.
    [5]
    Reigber A, Lombardini F, Viviani F, et al.. Three-dimensional and higher-order imaging with tomographic SAR: techniques, applications, issues[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 2915-2918.
    [6]
    Serafino F, Soldovieri F, Lombardini F, et al.. Singular value decomposition applied to 4D SAR imaging[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, 2005: 2701-2704.
    [7]
    孙希龙, 余安喜, 董臻, 等. 一种差分SAR层析高分辨成像方法[J]. 电子与信息学报, 2012, 34(2): 273-278. Sun Xi-long, Yu An-xi, Dong Zhen, et al.. A high resolution method for differential SAR tomography[J]. Journal of Electronics Information Technology, 2012, 34(2): 273-278.
    [8]
    任笑真, 杨汝良. 一种基于逆问题的差分干涉SAR层析成像方法[J]. 电子与信息学报, 2010, 32(3): 582-586. Ren Xiao-zhen and Yang Ru-liang. An inverse problem based approach for differential SAR tomography imaging[J]. Journal of Electronics Information Technology, 2010, 32(3): 582-586.
    [9]
    Candes 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.
    [10]
    Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [11]
    Zhu X X and Bamler R. Sparse reconstruction techniques for SAR tomography[C]. 17th International Coference on Digital Signal Processing, Corfu, Greece, 2011: 1-8.
    [12]
    Ren Xiao-zhen and Chen Li-na. Four-dimensional SAR imaging algorithm using Bayesian compressive sensing[J]. Journal of Electromagnetic Waves and Applications, 2014, 28(13): 1661-1676.
    [13]
    Cetin M and Karl W C. Feature enhanced synthetic aperture radar image formation based on non-quadratic regularization[J]. IEEE Transactions on Image Processing, 2001, 10(4): 623-631.
    [14]
    Samadi 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.
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