  Achieving high-resolution Direction of Arrival (DoA) recovery typically
requires high Signal to Noise Ratio (SNR) and a sufficiently large number of
snapshots. This paper presents NUV-DoA algorithm, that augments Bayesian sparse
reconstruction with spatial filtering for super-resolution DoA estimation. By
modeling each direction on the azimuth's grid with the sparsity-promoting
normal with unknown variance (NUV) prior, the non-convex optimization problem
is reduced to iteratively reweighted least-squares under Gaussian distribution,
where the mean of the snapshots is a sufficient statistic. This approach not
only simplifies our solution but also accurately detects the DoAs. We utilize a
hierarchical approach for interference cancellation in multi-source scenarios.
Empirical evaluations show the superiority of NUV-DoA, especially in low SNRs,
compared to alternative DoA estimators.
