  In this work, we consider direction-of-arrival (DoA) estimation in the
presence of extreme noise using Deep Learning (DL). In particular, we introduce
a Convolutional Neural Network (CNN) that is trained from mutli-channel data of
the true array manifold matrix and is able to predict angular directions using
the sample covariance estimate. We model the problem as a multi-label
classification task and train a CNN in the low-SNR regime to predict DoAs
across all SNRs. The proposed architecture demonstrates enhanced robustness in
the presence of noise, and resilience to a small number of snapshots. Moreover,
it is able to resolve angles within the grid resolution. Experimental results
demonstrate significant performance gains in the low-SNR regime compared to
state-of-the-art methods and without the requirement of any parameter tuning.
We relax the assumption that the number of sources is known a priori and
present a training method, where the CNN learns to infer the number of sources
jointly with the DoAs. Simulation results demonstrate that the proposed CNN can
accurately estimate off-grid angles in low SNR, while at the same time the
number of sources is successfully inferred for a sufficient number of
snapshots. Our robust solution can be applied in several fields, ranging from
wireless array sensors to acoustic microphones or sonars.
