Multichannel Blind Identification and Estimation: From Subspace to Maximum Likelihood Methods

Abstract A review of recent blind channel estimation algorithms is presented. From the (second-order) moment-based methods to the maximum likelihood approaches, under both statistical and deterministic signal models, we outline basic ideas behind several new developments, the assumptions and identifiability conditions required by these approaches, and the algorithm characteristics and performances.

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