论文标题
通过模拟大型天线阵列的MIMO雷达系统中DOA估计的深度学习
Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays
论文作者
论文摘要
我们通过使用深度学习来重建虚拟大型天线阵列的信号，提出了使用小天线阵列的基于音乐的到达方向（DOA）估计策略。所提出的策略不仅比简单地将传入信号插入音乐的性能要好得多，而且令人惊讶的是，性能也比直接使用带有音乐的实际大天线阵列的高角度范围和低测试SNR值的直接。我们进一步分析了训练SNR作为测试SNR的函数的最佳选择，并观察到此功能在不同角度范围内的行为发生了巨大变化。
We present a MUSIC-based Direction of Arrival (DOA) estimation strategy using small antenna arrays, via employing deep learning for reconstructing the signals of a virtual large antenna array. Not only does the proposed strategy deliver significantly better performance than simply plugging the incoming signals into MUSIC, but surprisingly, the performance is also better than directly using an actual large antenna array with MUSIC for high angle ranges and low test SNR values. We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.
