Optimal adaptive filter realizations for sample stochastic processes with an unknown parameter

Abstract

Techniques are given for realizing optimal learning systems for filtering a sampled stochastic process in the presence of an unknown constant or time-varying parameter. It is shown how the nonlinear Bayes optimal (quadratic sense) adaptive filters can be directly realized for continuous parameter spaces by real-time analog systems. Examples are given for both constant and time-varying unknown parameters.