Abstract：High-resolution synthetic aperture radar presents a significant challenge to imaging algorithms and computing power.Slide spotlight is an important mode that has both high resolution and wide azimuth swath. Generally,in the slide spotlight mode,the performance of conventional frequency domain imaging algorithms degrades because of orbit curvature,the time-variant azimuth chirp rate,and other factors.We adopt the Back-Projection (BP) algorithm in this study to counteract this limitation.We also propose a CPU/GPU heterogeneous BP algorithm to deal with the high computing complexity O (N3) of the BP algorithm.This heterogeneous BP algorithm makes full use of computing resources and accelerates imaging progress,and the design of a scheduling thread improves the flexibility of the algorithm.
邓云凯, 赵凤军, 王宇. 星载SAR技术的发展趋势及应用浅析[J]. 雷达学报, 2012, 1(1):1-10.Deng Yun-kai, Zhao Feng-jun, and Wang Yu. Brief analysis on the development and application of spaceborne SAR[J]. Journal of Radars, 2012, 1(1):1-10.
[2]
Werninghaus R. TerraSAR-X mission[C]. SAR Image Analysis, Modeling, and Techniques VI, Barcelona, Spain, 2003:9-16.
[3]
Mittermayer J, Lord R, and Borner E. Sliding spotlight SAR processing for TerraSAR-X using a new formulation of the extended chirp scaling algorithm[C]. 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003, 3:1462-1464.
[4]
Lanari R, Tesauro M, Sansosti E, et al.. Spotlight SAR data focusing based on a two-step processing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(9):1993-2004.
[5]
Xu Wei, Huang Ping-ping, and Deng Yun-kai. TOPSAR data focusing based on azimuth scaling preprocessing[J]. Advances in Space Research, 2011, 48(2):270-277.
[6]
Desai M D and Jenkins W K. Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar[J]. IEEE Transactions on Image Processing, 1992, 1(4):505-517.
[7]
Ozdemir C. Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms[M]. John Wiley & Sons, 2012.
[8]
Owens J D, Houston M, Luebke D, et al.. GPU computing[J]. Proceedings of the IEEE, 2008, 96(5):879-899.
[9]
Krakiwsky S E, Turner L E, and Okoniewski M M. Acceleration of Finite-Difference Time-Domain (FDTD) using Graphics Processor Units (GPU)[C]. 2004 IEEE MTT-S International Microwave Symposium Digest, 2004, 2:1033-1036.
[10]
Cire?an D, Meier U, Masci J, et al.. Multi-column deep neural network for traffic sign classification[J]. Neural Networks, 2012, 32:333-338.
[11]
Fasih A and Hartley T. GPU-accelerated synthetic aperture radar backprojection in CUDA[C]. 2010 IEEE Radar Conference, Washington, DC, 2010:1408-1413.
[12]
Capozzoli A, Curcio C, and Liseno A. Fast GPU-based interpolation for SAR backprojection[J]. Progress In Electromagnetics Research, 2013, 133:259-283.
[13]
丁金闪, Otmar L, Holger N, 等. 异构平台双基SAR成像的RD算法[J]. 电子学报, 2009, 37(6):1170-1173.Ding Jin-shan, Otmar L, Holger N, et al.. Focusing bistatic SAR data from herterogeneous platforms using the range Doppler algorithm[J]. Acta Electronica Sinica, 2009, 37(6):1170-1173.
[14]
Song Ming-cong, Liu Ya-bo, Zhao Feng-jun, et al..Processing of SAR data based on the heterogeneous architecture of GPU and CPU[C]. 2013 IET International Radar Conference, Xi'an, China, 2013:1-5.
[15]
Mittermayer J, Wollstadt S, Prats-Iraola P, et al.. The TerraSAR-X staring spotlight mode concept[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6):3695-3706.
[16]
Cumming I G and Wong F H. Digital Processing of Synthetic Aperture Radar Data:Algorithms and Implementation[M]. Artech House, 2005.
[17]
Gorham L R A and Moore L J. SAR image formation toolbox for MATLAB[C]. Algorithms for Synthetic Aperture Radar Imagery XVⅡ, Orlando, USA, 2010:769906.
[18]
Nickolls J and Dally W J. The GPU computing era[J]. IEEE Micro, 2010, 30(2):56-69.
[19]
Kirk D. NVIDIA CUDA software and GPU parallel computing architecture[C]. Proceedings of the 6th International Symposium on Memory Management, New York, USA, 2007, 7:103-104.
2017, Vol. 6 
