基于MN-MEA算法的无人机载SAR相位误差补偿处理

无人机载合成孔径雷达(synthetic aperture radar, SAR),在成像中更容易受到运动误差的干扰,造成图像质量下降。利用三维空间坐标系的斜距方程分离,可以为无人机载SAR成像提供更多的信息，但该方法并未考虑无人机载SAR成像中的运动误差补偿问题。针对无人机载机动SAR成像的相位误差补偿问题,进行相关研究。结合子图像划分,提出了基于迭代分块处理和误差相位初值模型的改进牛顿最小熵(modified Newton minimum entropy, MN-MEA)相位误差补偿算法,进一步校正了残余空变性误差和运动误差,改善成像质量。

Phase error compensation processing of UAV-borne SAR based on MN-MEA algorithm

Unmanned aerial vehicle (UAV) borne synthetic aperture radar (SAR) is more susceptible to motion errors in imaging, resulting in image quality degradation. More information can be provided for SAR imaging by using the separation of slant distance equations in the 3-D coordinate system. However, this method does not consider the problem of motion error compensation in UAV SAR imaging. In this paper, the phase error compensation of UAV mobile SAR imaging is studied. Combined with the sub-image segmentation, an improved modified Newton minimum entropy (MN-MEA) phase error compensation algorithm based on iterative block processing and the initial phase error model is proposed, which further corrects the residual spatiality error and motion error and improves the imaging quality.