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Linear estimation of signals is often based on covariance matrices estimated from training, which can perform poorly if the training data are limited and the estimated covariance matrices are ill-conditioned. Shrinking the covariance matrix toward a scaled identity matrix can improve the robustness against the model uncertainty provided the shrinkage factor is appropriately chosen. This paper introduces...
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