  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.
