关键词: 宽角合成孔径雷达, 高斯字典原子, 稀疏表示, 广义最小最大凹惩罚

Abstract: 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.

Key words: wide angle synthetic aperture radar (SAR), Gaussian dictionary atoms, sparse representation, generalized minimax concave (GMC) penalty