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Direction finding and positioning systems based on RF signals aresignificantly impacted by multipath propagation, particularly in indoorenvironments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angleof Arrival (AoA) in the presence of multipath or when operating in a weaksignal regime. We note that digitally sampled RF frontends allow for the easyanalysis of signals, and their delayed components. Low-cost Software-DefinedRadio (SDR) modules enable Channel State Information (CSI) extraction across awide spectrum, motivating the design of an enhanced Angle-of-Arrival (AoA)solution. We propose a Deep Learning approach to deriving AoA from a singlesnapshot of the SDR multichannel data. We compare and contrast deep-learningbased angle classification and regression models, to estimate up to two AoAsaccurately. We have implemented the inference engines on different platforms toextract AoAs in real-time, demonstrating the computational tractability of ourapproach. To demonstrate the utility of our approach we have collected IQ(In-phase and Quadrature components) samples from a four-element UniversalLinear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS)environments, and published the dataset. Our proposed method demonstratesexcellent reliability in determining number of impinging signals and realizedmean absolute AoA errors less than $2^{\circ}$.

DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks