计算机工程 ›› 2020, Vol. 46 ›› Issue (11): 306-314.doi: 10.19678/j.issn.1000-3428.0055955

• 开发研究与工程应用 • 上一篇    下一篇

基于GPU并行计算的雷达杂波模拟研究

徐国伟a,b, 陈建a, 成怡a,b   

  1. 天津工业大学 a. 电气工程与自动化学院;b. 天津市电工电能新技术重点实验室, 天津 300387
  • 收稿日期:2019-09-08 修回日期:2019-11-20 发布日期:2019-12-04
  • 作者简介:徐国伟(1972-),男,副教授、博士,主研方向为GPU加速、滑模控制;陈建,硕士研究生;成怡,副教授、博士。
  • 基金项目:
    天津市自然科学基金(17JCYBJC18500,17JCYBJC19400,18JCYBJC88400,18JCYBJC88300)。

Research on Radar Clutter Simulation Based on GPU Parallel Computing

XU Guoweia,b, CHEN Jiana, CHENG Yia,b   

  1. a. School of Electrical Engineering and Automation;b. Tianjin Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tiangong University, Tianjin 300387, China
  • Received:2019-09-08 Revised:2019-11-20 Published:2019-12-04

摘要: 现代雷达杂波模拟需使用杂波数据实时分析与处理回波信号,然而传统球不变随机过程(SIRP)方法生成杂波数据耗时较长。通过对SIRP方法进行改进,提出一种利用图形处理器(GPU)并行计算提升杂波生成实时性的方法。在计算统一设备架构(CUDA)下,对相关相干K分布杂波算法进行多任务串-并行分析,采用cuBLAS库对细粒度卷积计算进行优化,利用OpenMP+CUDA多任务调度机制改进粗粒度任务并行计算,以提高CPU-GPU利用率并减少数据等待时间。实验结果表明,该方法生成杂波数据的实时性显著提升,且随着杂波数据量增大其加速效果更好,相较传统GPU方法计算速率提高61%。

关键词: 雷达杂波, GPU并行计算, 球不变随机过程法, 卷积计算, cuBLAS库

Abstract: Modern radar clutter simulation needs to use clutter data for real-time analysis and processing of the echo signal.However,the traditional Spherically Invariant Random Process (SIRP) method for clutter data generation is time-consuming.By improving the SIRP method,this paper proposes a method to improve the real-time performance of clutter generation based on Graphic Processing Unit (GPU) parallel computing.In the Compute Unified Device Architecture (CUDA),the multi-task series-parallel analysis is carried out for the correlation coherent K-distribution clutter algorithm.In addition,the cuBLAS library is used to optimize the fine-grained convolution calculation,and the OpenMP + CUDA multi-task scheduling mechanism is used to improve the coarse-grained task parallel calculation in order to improve the CPU-GPU utilization and reduce the data waiting time.Experimental results show that compared with the traditional GPU method,the proposed method increases the calculation speed by 61%,and the real-time performance of clutter data generation is effectively improved.In addition,the acceleration effect significantly grows with the volume of clutter data.

Key words: radar clutter, GPU parallel computing, Spherically Invariant Random Process(SIRP) method, convolution calculation, cuBLAS library

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