Maximum likelihood covariance matrix estimation for complex elliptically symmetric distributions under mismatched conditions

摘要

This paper deals with the maximum likelihood (ML) estimation of scatter matrix of complex elliptically symmetric (CES) distributed data when the hypothesized and the true model belong to the CES family but are different, then under mismatched model condition. Firstly, we derive the Huber limit, or sandwich matrix expression, for a generic CES model. Then, we compare the performance of mismatched and matched ML estimators to the Huber limit and to the Cramer-Rao lower bound (CRLB) in some relevant study cases.