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Line spectral estimation (LSE) from multi snapshot samples is studiedutilizing the variational Bayesian methods. Motivated by the recently proposedvariational line spectral estimation (VALSE) method for a single snapshot, wedevelop the multisnapshot VALSE (MVALSE) for multi snapshot scenarios, which isimportant for array processing. The MVALSE shares the advantages of the VALSEmethod, such as automatically estimating the model order, noise variance andweight variance, closed-form updates of the posterior probability densityfunction (PDF) of the frequencies. By using multiple snapshots, MVALSE improvesthe recovery performance and it encodes the prior distribution naturally.Finally, numerical results demonstrate the competitive performance of theMVALSE compared to state-of-the-art methods.

Gridless Line Spectral Estimation with Multiple Measurement Vector via Variational Bayesian Inference