  A vector sensor, a type of sensor array with six collocated antennas to
measure all electromagnetic field components of incident waves, has been shown
to be advantageous in estimating the angle of arrival and polarization of the
incident sources. While angle estimation with machine learning for linear
arrays has been well studied, there has not been a similar solution for the
vector sensor. In this paper, we propose neural networks to determine the
number of the sources and estimate the angle of arrival of each source, based
on the covariance matrix extracted from received data. Also, we provide a
solution for matching output angles to corresponding sources and examine the
error distributions with this method. The results show that neural networks can
achieve reasonably accurate estimation with up to 5 sources, especially if the
field-of-view is limited.
