论文标题
使用神经网络的到达估计的多个角度
Multiple Angles of Arrival Estimation using Neural Networks
论文作者
论文摘要
通过旋转不变性（ESPRIT）通过旋转（ESPRIT）对信号参数的多个信号分类（音乐）已被广泛用于均匀线性阵列（ULA）或均匀圆形阵列（UCA）的超级分辨率估计方向（DOA）。但是，当源信号的数量增加时，问题会变得具有挑战性，当找到峰值时，音乐会遭受计算复杂性，而ESPRIT可能对数量几何偏移可能不健壮。因此，神经网络成为潜在的解决方案。在本文中，我们提出了一个神经网络，以根据从接收到的数据中提取的相关矩阵来估计方位角和高度角度。此外，列出了串行方案以估计多个信号案例。结果表明，神经网络可以在低SNR下实现准确的估计并处理多个信号。
MUltiple SIgnal Classification (MUSIC) and Estimation of signal parameters via rotational via rotational invariance (ESPRIT) has been widely used in super resolution direction of arrival estimation (DoA) in both Uniform Linear Arrays (ULA) or Uniform Circular Arrays (UCA). However, problems become challenging when the number of source signal increase, MUSIC suffer from computation complexity when finding the peaks, while ESPRIT may not robust to array geometry offset. Therefore, Neural Network become a potential solution. In this paper, we propose a neural network to estimate the azimuth and elevation angles, based on the correlated matrix extracted from received data. Also, a serial scheme is listed to estimate multiple signals cases. The result shows the neural network can achieve an accurate estimation under low SNR and deal with multiple signals.
