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.
ML Estimate and CRLB of Covariance Matrix for Complex Elliptically Symmetric Distribution
GRECO, MARIA;GINI, FULVIO;
2013-01-01
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.File in questo prodotto:
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