We consider problems of estimation of structured covariance matrices, and in
particular of matrices with a Toeplitz structure. We follow a geometric
viewpoint that is based on some suitable notion of distance. To this end, we
overview and compare several alternatives metrics and divergence measures. We
advocate a specific one which represents the Wasserstein distance between the
corresponding Gaussians distributions and show that it coincides with the
so-called Bures/Hellinger distance between covariance matrices as well. Most
importantly, besides the physically appealing interpretation, computation of
the metric requires solving a linear matrix inequality (LMI). As a consequence,
computations scale nicely for problems involving large covariance matrices, and
linear prior constraints on the covariance structure are easy to handle. We
compare this transportation/Bures/Hellinger metric with the maximum likelihood
and the Burg methods as to their performance with regard to estimation of power
spectra with spectral lines on a representative case study from the literature.Comment: 12 pages, 3 figure
