ML estimate and CRLB of Covariance Matrix for Complex Elliptically Symmetric distribution

Maria Greco, Fulvio Gini, Ami Wiesel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper derives the 'constrained' maximum likelihood (ML) estimators and the Cramér-Rao Lower Bounds (CRLB) for the scatter matrix of Complex Elliptically Symmetric distributions and compares them in the particular cases of complex Gaussian, Generalized Gaussian (GG) and t-distributed observation vectors. Numerical results confirm the goodness of the ML estimators and the advantage of a constraint on the matrix trace for small data size.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: 9 Sep 201313 Sep 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period9/09/1313/09/13

Keywords

  • CRLB
  • Matrix estimation
  • complex elliptically distribution

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