  Line spectral estimation (LSE) from multi snapshot samples is studied
utilizing the variational Bayesian methods. Motivated by the recently proposed
variational line spectral estimation (VALSE) method for a single snapshot, we
develop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which is
important for array processing. The MVALSE shares the advantages of the VALSE
method, such as automatically estimating the model order, noise variance and
weight variance, closed-form updates of the posterior probability density
function (PDF) of the frequencies. By using multiple snapshots, MVALSE improves
the recovery performance and it encodes the prior distribution naturally.
Finally, numerical results demonstrate the competitive performance of the
MVALSE compared to state-of-the-art methods.
