GPU-Optimized Implementation for Accelerating CSAR Imaging

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Publicat a:Electronics vol. 14, no. 10 (2025), p. 2073
Autor principal: Cui Mengting
Altres autors: Li, Ping, Bu Zhaohui, Meng, Xun, Ding, Li
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MDPI AG
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024 7 |a 10.3390/electronics14102073  |2 doi 
035 |a 3211940617 
045 2 |b d20250101  |b d20251231 
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100 1 |a Cui Mengting  |u Institute of Biomedical Engineering, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 
245 1 |a GPU-Optimized Implementation for Accelerating CSAR Imaging 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The direct porting of the Range Migration Algorithm to GPUs for three-dimensional (3D) cylindrical synthetic aperture radar (CSAR) imaging faces difficulties in achieving real-time performance while the architecture and programming models of GPUs significantly differ from CPUs. This paper proposes a GPU-optimized implementation for accelerating CSAR imaging. The proposed method first exploits the concentric-square-grid (CSG) interpolation to reduce the computational complexity for reconstructing a uniform 2D wave-number domain. Although the CSG method transforms the 2D traversal interpolation into two independent 1D interpolations, the interval search to determine the position intervals for interpolation results in a substantial computational burden. Therefore, binary search is applied to avoid traditional point-to-point matching for efficiency improvement. Additionally, leveraging the partition independence of the grid distribution of CSG, the 360° data are divided into four streams along the diagonal for parallel processing. Furthermore, high-speed shared memory is utilized instead of high-latency global memory in the Hadamard product for the phase compensation stage. The experimental results demonstrate that the proposed method achieves CSAR imaging on a <inline-formula>1440×100×128</inline-formula> dataset in 0.794 s, with an acceleration ratio of 35.09 compared to the CPU implementation and 5.97 compared to the conventional GPU implementation. 
653 |a Parallel processing 
653 |a Central processing units--CPUs 
653 |a Algorithms 
653 |a Libraries 
653 |a Radar imaging 
653 |a Fourier transforms 
653 |a Real time 
653 |a Graphics processing units 
653 |a Interpolation 
653 |a Synthetic aperture radar 
653 |a Efficiency 
700 1 |a Li, Ping  |u Terahertz Technology Innovation Research Institute, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 
700 1 |a Bu Zhaohui  |u Institute of Biomedical Engineering, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 
700 1 |a Meng, Xun  |u Institute of Biomedical Engineering, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 
700 1 |a Ding, Li  |u Institute of Biomedical Engineering, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 
773 0 |t Electronics  |g vol. 14, no. 10 (2025), p. 2073 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3211940617/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3211940617/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3211940617/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch