Mismatched MMSE estimation of multivariate Gaussian sources

Iñaki Esnaola, Antonia M. Tulino, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The distortion increase in minimum mean-square error (MMSE) estimation of multivariate Gaussian sources is analyzed for the situation in which the statistics are mismatched, i.e., the covariance matrix is not perfectly known during the estimation process. First a deterministic mismatch model with an additive perturbation matrix is considered, for which we provide closed form expressions for the distortion excess caused by the mismatch. The mismatch study is then generalized by using random matrix theory tools which allow an asymptotic result for a broad class of perturbation matrices to be proved.

Original languageEnglish (US)
Title of host publication2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages716-720
Number of pages5
DOIs
StatePublished - Oct 22 2012
Event2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States
Duration: Jul 1 2012Jul 6 2012

Publication series

NameIEEE International Symposium on Information Theory - Proceedings

Other

Other2012 IEEE International Symposium on Information Theory, ISIT 2012
CountryUnited States
CityCambridge, MA
Period7/1/127/6/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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