Yu Ze, Lin Peng, Xiao Peng, Kang Lihong, Li Chunsheng
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China.
Beijing Institute of Remote Sensing Information, Beijing 100192, China.
Sensors (Basel). 2016 Jul 14;16(7):1091. doi: 10.3390/s16071091.
Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath.
与低地球轨道合成孔径雷达(SAR)相比,地球同步(GEO)SAR可以具有更短的重访周期和更广阔的覆盖范围。然而,这种SAR与目标之间的相对运动更为复杂,这使得距离单元徙动(RCM)在距离和方位向上都具有空间变异性。结果,高效精确的成像变得困难。本文在时域和频域对GEO SAR的空间变异性进行了分析和建模。提出了一种用于GEO SAR成像的新算法,该算法在地面横向距离和距离方向上的分辨率均为2 m,由五个步骤组成。第一步是通过第一次方位时间缩放消除线性方位变异性。第二步是实现RCM校正和距离压缩。第三步是通过第二次方位时频缩放校正残余方位变异性。第四步也是最后一步是完成方位聚焦并校正几何失真。该算法最重要的创新是实现了时频缩放以校正高阶方位变异性。仿真结果表明,该算法能够在整个测绘带内实现具有良好且均匀成像质量的GEO SAR成像。