  In this paper, we present a machine learning approach for estimating the
number of incident wavefronts in a direction of arrival scenario. In contrast
to previous works, a multilayer neural network with a cross-entropy objective
is trained. Furthermore, we investigate an online training procedure that
allows an adaption of the neural network to imperfections of an antenna array
without explicitly calibrating the array manifold. We show via simulations that
the proposed method outperforms classical model order selection schemes based
on information criteria in terms of accuracy, especially for a small number of
snapshots and at low signal-to-noise-ratios. Also, the online training
procedure enables the neural network to adapt with only a few online training
samples, if initialized by offline training on artificial data.
