基于噪声子空间的DOA估计算法在CPU和GPU上的性能分析
Performance Analysis of Noise Subspace-based Narrowband Direction-of-Arrival (DOA) Estimation Algorithms on CPU and GPU
摘要：
阵列信号处理问题的高性能计算是一项关键任务，因为许多应用都需要实时系统性能。基于噪声子空间的波达方向（DOA）估计算法因其具有较高的角分辨率和鲁棒性而受到广泛关注。在这项研究中，我们研究了各种优化策略的高性能DOA估计GPU和比较分析替代实现（MATLAB，C/C++和CUDA）。实验表明，与基准多线程CPU实现相比，GPU上可以实现高达3.1倍的加速比。源代码可在以下链接中公开获得：https://github.com/erayhamza/NssDOACuda
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
