论文标题
基于噪声子空间的窄带方向（DOA）估计算法的性能分析CPU和GPU
Performance Analysis of Noise Subspace-based Narrowband Direction-of-Arrival (DOA) Estimation Algorithms on CPU and GPU
论文作者
论文摘要
数组信号处理问题的高性能计算是一项关键任务，因为许多应用程序需要实时系统性能。基于噪声子空间的排序方向（DOA）估计算法在文献中很受欢迎，因为它们提供了更高的角度分辨率和更高的鲁棒性。在这项研究中，我们研究了对GPU高性能DOA估计的各种优化策略，并相对分析了替代实现（MATLAB，C/C ++和CUDA）。实验表明，与基线多线CPU实现相比，GPU上最多可以达到3.1倍的速度。源代码可在以下链接上公开可用：https：//github.com/erayhamza/nssdoacuda
High-performance computing of array signal processing problems is a critical task as real-time system performance is required for many applications. Noise subspace-based Direction-of-Arrival (DOA) estimation algorithms are popular in the literature since they provide higher angular resolution and higher robustness. In this study, we investigate various optimization strategies for high-performance DOA estimation on GPU and comparatively analyze alternative implementations (MATLAB, C/C++ and CUDA). Experiments show that up to 3.1x speedup can be achieved on GPU compared to the baseline multi-threaded CPU implementation. The source code is publicly available at the following link: https://github.com/erayhamza/NssDOACuda
