基于GPU并行计算的雷达杂波模拟研究

Research on Radar Clutter Simulation Based on GPU Parallel Computing

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%。

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.