  The problem of estimating the number of sources and their angles of arrival
from a single antenna array observation has been an active area of research in
the signal processing community for the last few decades. When the number of
sources is large, the maximum likelihood estimator is intractable due to its
very high complexity, and therefore alternative signal processing methods have
been developed with some performance loss. In this paper, we apply a deep
neural network (DNN) approach to the problem and analyze its advantages with
respect to signal processing algorithms. We show that an appropriate designed
network can attain the maximum likelihood performance with feasible complexity
and outperform other feasible signal processing estimation methods over various
signal to noise ratios and array response inaccuracies.
