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We consider the problem of estimating the covariance matrix RT of an observation vector, using K groups of snapshots Zk = [zk.1 ... zk.Lk], of respective size Lk, whose covariance matrices Rk are randomly distributed around Rt, and hence are different from Rt. The Cramer-Rao bound (CRB) for estimation of Rt is derived as well as its maximum likelihood estimator (MLE). We illustrate the behavior of the CRB in the two opposite cases, namely K = 1 where all snapshots share a common covariance matrix, and Lk = 1 where each snapshot has a different covariance matrix. We also discuss the influence of the degree of heterogeneity on the estimation performance.
Date of Conference: 4-7 Nov. 2007