稀疏条件下基于散射点估计的SAR切片超分辨重建

从合成孔径雷达(SAR)成像模型出发，在稀疏条件下，该文结合散射中心理论，从低分辨率图像中估计高分辨率图像的散射点参数，用若干sinc函数对感兴趣目标区(ROI)进行重建并抑制旁瓣，获得超分辨ROI切片。基于非线性最小二乘(NLS)估计给出了该超分辨重建问题的迭代求解算法，并以TerraSAR-X数据进行仿真验证，仿真结果表明，该文所提方法相比双立方插值和1范数正则化方法能够获得更高的空间分辨率与目标杂波比(TCR)。后续分析表明，散射点参数的估计精度受到信噪比和sinc函数重建3 dB带宽共同影响，重建3 dB带宽越大对噪声的鲁棒性越强。

Super-resolution Reconstruction of SAR Section Based on Scattering Center Estimation and Sparse Constraint

From the SAR imaging model, combining the scattering center theory, this paper estimates scattering centers of high resolution image from the low resolution image under the conditions of sparse. The Region Of Interesting (ROI) can be reconstructed by several sinc functions and the super resolution section is obtained after side lobe suppression. Based on the Nonlinear Least Squares (NLS) estimation, an iterative algorithm is employed to solve the super resolution reconstruction problem and the simulations are based on TerraSAR-X measurement data. Simulation results show that the proposed method is able to get higher spatial resolution and Target to Clutter Ratio (TCR) values as compared with bicubic interpolation and 1 norm regularization method. The analysis results show that the accuracy of the algorithm is affected by both the Signal to Noise Ratio (SNR) and the rebuilding 3 dB bandwidth of sinc function, the higher 3 dB bandwidth tends to be more robust to noise.