  We propose two algorithms that use both models and datasets to estimate
angular power spectra from channel covariance matrices in massive MIMO systems.
The first algorithm is an iterative fixed-point method that solves a
hierarchical problem. It uses model knowledge to narrow down candidate angular
power spectra to a set that is consistent with a measured covariance matrix.
Then, from this set, the algorithm selects the angular power spectrum with
minimum distance to its expected value with respect to a Hilbertian metric
learned from data. The second algorithm solves an alternative optimization
problem with a single application of a solver for nonnegative least squares
programs. By fusing information obtained from datasets and models, both
algorithms can outperform existing approaches based on models, and they are
also robust against environmental changes and small datasets.
