基于GPU加速的脉冲多普勒雷达信号处理

雷达信号处理算法的高性能实现是雷达系统中的关键技术。传统雷达信号处理算法的高性能加速主要依赖DSP和FPGA等专用设备，而它们具有开发周期长、调试难度大、成本高等缺点。GPU作为通用设备，特别适合处理雷达信号这种大规模数据。目前，GPU加速雷达信号处理的成果大多集中在SAR成像等应用领域，针对脉冲多普勒雷达相关研究还比较少。为了满足雷达回波数据对吞吐量和处理实时性的高要求，提出了基于网格跨步并行的细粒度并行化、基于多CUDA流的粗粒度并行化和基于并行扫描的数据预处理等优化技术。 从性能测试和误差分析等多角度评估了算法的实时性和准确性，在所使用的硬件平台上相比于传统CPU实现达到了300倍以上的加速比，并优于其它已有的CUDA加速的脉冲多普勒雷达信号处理算法。

GPU-accelerated pulse Doppler radar signal processing

High-performance radar signal processing is a key technique in radar systems. Previous research on high-performance radar signal processing algorithms usually rely on specialized signal processing devices, such as FPGAs or DSPs, which are not only expensive but also difficult to program. GPU is a general-purpose device, especially suitable for processing large-scale data, such as radar signals, etc. At present, most of the existing GPU-accelerated radar signal processing research is on SAR imaging related applications, with little research on target detection radar, such as pulsed Doppler radar. In order to fulfill the requirement of radar echo data on throughput and real-time processing, a novel fine-grained parallelization by grid stride, a coarse-grained parallelization by multi-CUDA streams, and a data preprocessing method based on parallel scan are proposed. The real-time performance and accuracy of the algorithm are evaluated from multiple perspectives such as performance testing and error analysis. Compared with the state-of-the-art implementation on CPU, the hardware platform has achieved a speedup of more than 300 times, and is superior than other existing CUDA accelerated pulse Doppler radar signal processing algorithms.