Performance degradation of DOA estimators due to unknown noise fields

A statistical performance analysis of subspace-based directions-of-arrival (DOA) estimation algorithms in the presence of correlated observation noise with unknown covariance is presented. The analysis of five different estimation algorithms is unified by a single expression for the mean-squared DOA estimation error which is derived using a subspace perturbation expansion. The analysis assumes that only a finite amount of array data is available.<
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