GPU-Optimized Implementation for Accelerating CSAR Imaging
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| Publicat a: | Electronics vol. 14, no. 10 (2025), p. 2073 |
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| Autor principal: | |
| Altres autors: | , , , |
| Publicat: |
MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3211940617 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14102073 |2 doi | |
| 035 | |a 3211940617 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 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 |