High-precision wide angle SAR imaging method based on sparse representation of Gaussian dictionary atoms [基于高斯字典原子稀疏表示的高精度宽角SAR成像方法]

作者：Chen C.; Wei Z.; Xu Z.; Zhang B.

来源：Systems Engineering and Electronics, 2019, 41(11): 2471-2478.

DOI：10.3969/j.issn.1001-506X.2019.11.10

• Gaussian dictionary atoms
• Generalized minimax concave (GMC) penalty
• Sparse representation
• Wide angle synthetic aperture radar (SAR)

In order to improve the performance of extracting the anisotropic properties of target backscattering from wide angle synthetic aperture radar images, a high-precision wide angle synthetic aperture radar imaging method based on sparse representation of Gaussian dictionary atoms is proposed. In the dictionary construction, the method uses Gaussian functions with different center positions and the same variance. In solving the sparse representation coefficients, the method uses the generalized minimax concave penalty sparse reconstruction algorithm. Finally, the anisotropic properties of the target backscattering are obtained according to the reconstruction result of the sparse representation coefficients and the constructed dictionary. The method is validated by simulation experiments and the Backhoe data. The results show that the proposed method can extract the anisotropic properties of the target backscattering with high precision.

• 单位
  中国科学院大学