Performance bounds for parameter estimation from time-continuous observations

Abstract

In this paper we derive recursive expression for certain distance measures between time-continuous, stationary, vector Gaussian processes, and then utilize them to derive upper bounds to the mean square error performance of the Bayes and Maximum Likelihood estimate of a parameter, when only a finite-valued parameter set is utilized. The question of convergence when the true parameter value does not belong to the finite set is also answered.