Synthetic aperture radar (SAR) is considered a prevailing tool for remote sensing. It benefits working with high efficiency in all weather and all-day circumstances, making SAR is very confident compared to other types of remote sensing. The SAR platform moves with constant velocity and height, with a linear path for ideal situations. However, this assumption is not realized in satellite movement, which is an elliptical orbiting that worsens the quality of the focused image. This paper introduces a methodology of motion compensation for motion errors due to satellite elliptical orbiting and perturbations in an orbital path. It represents two major contributions applied on a low earth orbit (LEO) spaceborne SAR. First, motion errors analysis in the range and azimuth directions. Second, an algorithm for motion error compensation (MOCO) combined with a chirp scaling algorithm (CSA) is performed. Moreover, a validation for the formulated algorithm is executed using sentinel-1 level-0 real raw data input, and the result is compared with the sentinel-1 level-1 single look complex (SLC) SAR image. The validation is performed using two different metrics. First, image quality measurement by sharpness, contrast, and entropy. Second, measuring the peak-sidelobe-ratio (PSLR), impulse-response-width (IRW), and integrated-sidelobe-ratio (ISLR) for five high power reflecting points in the scene area.

合成孔径雷达（SAR）被认为是遥感的主要工具。它有利于在全天候、全天的情况下高效工作，与其他类型的遥感相比，SAR非常有信心。SAR 平台以恒定的速度和高度移动，在理想情况下具有线性路径。然而，这一假设在卫星运动中并未实现，卫星运动是一种椭圆轨道，会降低聚焦图像的质量。本文介绍了一种对由于卫星椭圆轨道和轨道路径扰动引起的运动误差进行运动补偿的方法。它代表了应用于低地球轨道 (LEO) 星载 SAR 的两个主要贡献。首先，距离和方位方向的运动误差分析。第二，执行运动误差补偿 (MOCO) 与线性调频缩放算法 (CSA) 相结合的算法。此外，使用 sentinel-1 level-0 真实原始数据输入对公式化算法进行验证，并将结果与​​ sentinel-1 level-1 单视复杂 (SLC) SAR 图像进行比较。使用两个不同的指标执行验证。首先，通过锐度、对比度和熵来测量图像质量。其次，测量场景区域内五个高功率反射点的峰值旁瓣比（PSLR）、脉冲响应宽度（IRW）和综合旁瓣比（ISLR）。使用两个不同的指标执行验证。首先，通过锐度、对比度和熵来测量图像质量。其次，测量场景区域内五个高功率反射点的峰值旁瓣比（PSLR）、脉冲响应宽度（IRW）和综合旁瓣比（ISLR）。使用两个不同的指标执行验证。首先，通过锐度、对比度和熵来测量图像质量。其次，测量场景区域内五个高功率反射点的峰值旁瓣比（PSLR）、脉冲响应宽度（IRW）和综合旁瓣比（ISLR）。