Information Inequality for Estimation of Transfer Functions: Main Results

In this paper we derive a canonical lower bound for the autocovariance function of any unbiased transfer-function estimator. As a generalization of the Cramér-Rao bound, the Cramér-Rao kernel that we define can be derived without parametrizing the model set. The Cramér-Rao kernel is thus one of the cornerstones for experiment design formulations that do not depend on the choice of coordinates.