  Direction finding and positioning systems based on RF signals are
significantly impacted by multipath propagation, particularly in indoor
environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle
of Arrival (AoA) in the presence of multipath or when operating in a weak
signal regime. We note that digitally sampled RF frontends allow for the easy
analysis of signals, and their delayed components. Low-cost Software-Defined
Radio (SDR) modules enable Channel State Information (CSI) extraction across a
wide spectrum, motivating the design of an enhanced Angle-of-Arrival (AoA)
solution. We propose a Deep Learning approach to deriving AoA from a single
snapshot of the SDR multichannel data. We compare and contrast deep-learning
based angle classification and regression models, to estimate up to two AoAs
accurately. We have implemented the inference engines on different platforms to
extract AoAs in real-time, demonstrating the computational tractability of our
approach. To demonstrate the utility of our approach we have collected IQ
(In-phase and Quadrature components) samples from a four-element Universal
Linear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS)
environments, and published the dataset. Our proposed method demonstrates
excellent reliability in determining number of impinging signals and realized
mean absolute AoA errors less than $2^{\circ}$.
