计算机工程 ›› 2020, Vol. 46 ›› Issue (10): 240-247.doi: 10.19678/j.issn.1000-3428.0056306

• 体系结构与软件技术 • 上一篇    下一篇

基于ARM GPU的机载SAR成像算法并行优化策略

李威1,2, 梁军1,2, 张桢1,2, 李青1,2   

  1. 1. 北京联合大学 北京市信息服务工程重点实验室, 北京 100101;
    2. 北京联合大学 机器人学院, 北京 100027
  • 收稿日期:2019-10-16 修回日期:2019-12-23 发布日期:2019-12-24
  • 作者简介:李威(1995-),男,硕士研究生,主研方向为并行算法优化;梁军(通信作者),教授;张桢,硕士研究生;李青,副教授。
  • 基金项目:
    国家自然科学青年基金(61502036);北京联合大学学科定位十大前沿方向专项(ZK40201901)。

Parallel Optimization Strategy of Airborne SAR Imaging Algorithm Based on ARM GPU

LI Wei1,2, LIANG Jun1,2, ZHANG Zhen1,2, LI Qing1,2   

  1. 1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China;
    2. College of Robotics, Beijing Union University, Beijing 100027, China
  • Received:2019-10-16 Revised:2019-12-23 Published:2019-12-24

摘要: 随着无人机技术的快速发展,机载合成孔径雷达(SAR)以高分辨率、高机动性和低成本等特点成为多云雾山丘地区的主要遥感手段,但机载SAR计算资源有限且分析过程需要耗费大量时间,因此降低了无人机对外界环境的响应能力。针对机载SAR成像过程中的多视处理、旋转放缩和图像量化算法,从简化计算、优化访存和减少条件分支3个方面出发,在ARM Mali-T860 GPU架构上实现基于OpenCL的并行优化策略。实验结果表明,与基于CPU的SAR成像算法相比,优化的多视处理、旋转放缩和图像量化算法分别取得了17倍~62倍、48倍~74倍及31倍~33倍的计算性能提升,且能够实现跨平台应用。

关键词: 合成孔径雷达, OpenCL平台, 向量化, 访存优化, 多视处理

Abstract: With the rapid development of drone technology,airborne Synthetic Aperture Radar(SAR) has become the main remote sensing solution for cloudy and hilly areas due to its high resolution,high maneuverability,and low cost.However,the computing resources of airborne SAR are limited and its analysis process is time-consuming,which reduces the responsiveness of drones to external environment.Therefore,this paper describes the implementation of an OpenCL-based parallel optimization strategy on the ARM Mali-T860 GPU architecture for the multi-view processing,rotation scaling and image quantization algorithms in airborne SAR imaging.The optimization strategy is designed to simplify calculations,optimize memory access,and reduce conditional branches.Experimental results show that compared with the CPU-based SAR imaging algorithms,the performance of optimized multi-view processing,rotation scaling and image quantization algorithms is 17~62 times,48~74 times,and 31~33 times respectively what it was,and the optimized algorithms can be used for cross-platform applications.

Key words: Synthetic Aperture Radar(SAR), OpenCL platform, vectorization, memory access optimization, multi-view processing

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