GPU-Optimized Implementation for Accelerating CSAR Imaging
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| Publicado en: | Electronics vol. 14, no. 10 (2025), p. 2073 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
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| Resumen: | 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. |
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| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics14102073 |
| Fuente: | Advanced Technologies & Aerospace Database |