An optimal linear filter for estimation of random functions in Hilbert space

Abstract

Let ${\mbox{$\mbox{\boldmath $f$}$}}$ be a square-integrable, zero-mean, random vector with observable realizations in a Hilbert space $H$, and let ${\mbox{$\mbox{\boldmath $g$}$}}$ be an associated square-integrable, zero-mean, random vector with realizations, which are not observable, in a Hilbert space $K$. We seek an optimal filter in the form of a closed linear operator $X$ acting on the observable realizations of a proximate vector ${\mbox{$\mbox{\boldmath $f$}$}}_ε \approx {\mbox{$\mbox{\boldmath $f$}$}}$ that provides the best estimate $\widehat{{\mbox{$\mbox{\boldmath $g$}$}}}_ε = X {\mbox{$\mbox{\boldmath $f$}$}}_ε$ of the vector ${\mbox{$\mbox{\boldmath $f$}$}}$. We assume the required covariance operators are known. The results are illustrated with a typical example.