Estimation for the Linear Model With Uncertain Covariance Matrices
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 6, 1525-1535 p.Article in journal (Refereed) Published
We derive a maximum a posteriori estimator for the linear observation model, where the signal and noise covariance matrices are both uncertain. The uncertainties are treated probabilistically by modeling the covariance matrices with prior inverse-Wishart distributions. The nonconvex problem of jointly estimating the signal of interest and the covariance matrices is tackled by a computationally efficient fixed-point iteration as well as an approximate variational Bayes solution. The statistical performance of estimators is compared numerically to state-of-the-art estimators from the literature and shown to perform favorably.
Place, publisher, year, edition, pages
2014. Vol. 62, no 6, 1525-1535 p.
Maximum a posteriori estimation, covariance matrices, inverse Wishart
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-144379DOI: 10.1109/TSP.2014.2301973ISI: 000333025000016ScopusID: 2-s2.0-84896445021OAI: oai:DiVA.org:kth-144379DiVA: diva2:713242
FunderEU, FP7, Seventh Framework Programme, 228044Swedish Research Council, 621-2011-5847 621-2012-4134
QC 201404222014-04-222014-04-222014-04-22Bibliographically approved