复杂轨迹合成孔径雷达后向投影算法图像流GPU成像

相对于基于傅里叶变换的频域成像算法，后向投影(BP)算法因采用时域逐点相干积累，更适合于复杂轨迹合成孔径雷达(SAR)高精度成像。但BP算法计算量巨大，限制了其应用于SAR大场景大数据量快速成像。图形处理器(GPU)具有强大浮点运算和并行处理能力，为大场景BP算法快速成像实现提供了途径。结合GPU并行处理，提出了一种基于图像流的复杂运动SAR大场景BP快速成像处理方法。该方法借助BP算法中图像像素点相互独立处理的特性，采用图像像素点并行及图像流程处理，设计了孔径与图像缓存调度方案，提高SAR大场景大数据BP算法成像效率。仿真和机载实测数据结果验证了方法的有效性，在有限GPU显存条件下实现了8 192×8 192大场景快速成像，并且成像加速比相对于传统CPU单线程处理可达300倍以上。

Image streams GPU-based back projection for complex trajectory synthetic aperture radar imaging

Compared with the frequency domain algorithms based on Fourier transform,back projection(BP) algorithm is more applicable for high-precision synthetic aperture radar(SAR) imaging with complex trajectory because of its time-domain coherent integration point by point.However,BP algorithm has a problem of huge calculation quantity,which restricts its application to fast SAR imaging with large scene and big data.Graphics processing unit(GPU) has a strong ability in floating-point calculation and parallel processing,and provides an effective approach for large-scene fast imaging with BP algorithm.According to GPU parallel processing,this paper proposes a new processing method of large-scene SAR fast imaging with BP algorithm,based on complex motion of image flow.Depending on the character that the image pixels in BP algorithm are processed independently,this method uses the parallelization of image pixels and image-flow processing to design a scheduling scheme between apertures and image caches to improve the imaging efficiency of SAR with large scene and big data.Simulation and airborne experimental results demonstrate the effectiveness of the method,and fast imaging with the large scene of 8 192×8 192 has been realized in the condition of limited GPU memory,and the speed-up ratio can be improved 300 times compared with the traditional CPU single-thread processing.