Ricatti stochastic filter as an estimator

Abstract

The Ricatti stochastic digital filter as an estimator is based on a first differences order model with uncorrelated innovation properties bounding the spatial operation region with two auxiliary equations. This permits optimal results having the traditional inversion instead of the pseudo-inverse strategy. The stationary conditions and uncorrelated trajectories were the tools applied in adaptive estimation and identification integrated form. In spite of the blackbox form observing the output system, the parametre and identification simulation achieved a great convergence rate in agreement with functional error. It was built considering the identification second probability moment defined as the difference between the desired signal and the output filter response. The parametres estimated were inside the unit circle and had a great advantage because the primitive solution depends on their values.