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

中图分类号: 

• TP391