  In this paper, we study the problem of direction of arrival estimation and
model order selection for systems employing subarray sampling. Thereby, we
focus on scenarios, where the number of active sources is not smaller than the
number of simultaneously sampled antenna elements. For this purpose, we propose
new schemes based on neural networks and estimators that combine neural
networks with gradient steps on the likelihood function. These methods are able
to outperform existing estimators in terms of mean squared error and model
selection accuracy, especially in the low snapshot domain, at a drastically
lower computational complexity.
