一种基于极坐标格式算法的高分辨SAR成像自聚焦算法

针对机载聚束合成孔径雷达(SAR)惯导精度无法满足高分辨SAR成像的问题，该文提出了一种结合极坐标格式算法(PFA)的自聚焦算法，即由粗到精的混合多阶段参数化最小熵(Hybrid Multistage Parameterized Minimum Entropy, HMPME)距离单元徙动校正方法和基于图像对比度增强(Contrast Enhancement, CE)的变步长迭代相位误差校正方法。该自聚焦算法可以直接嵌入到PFA处理中，精确地补偿了惯导测量精度不足引起的越距离单元徙动(Range Cell Migration, RCM)和相位误差，且对于低对比度、低信噪比场景数据有良好的聚焦性能。最后，利用仿真实验和实测机载聚束SAR数据验证了所提算法的有效性。

Autofocus Algorithm of High Resolution SAR Based on Polar Format Algorithm Processing

Equipped with an airborne spotlight SAR, inertia navigation system can not measure the motion errors with the required accuracy for high resolution SAR imaging, which may degrade severely the quality of a SAR image. In this paper, a novel autofocus algorithm that can be directly embedded in Polar Format Algorithm (PFA) is proposed, that is, a Hybrid Multistage Parameterized Minimum Entropy (HMPME) algorithm to proceed Range Cell Migration (RCM) correction and a scaled-stepsize iterative phase correction method based on Contrast Enhancement (CE). The autofocus processing accurately compensates the effects of range cell migration and phase errors. Also, this algorithm is most robust in processing images of low contrast and low signal-to-noise ratio. Finally, simulations and experiments with real spotlight SAR data validate the effectiveness of the proposed algorithm.