Optimal filtering algorithm using covariance information in linear discrete-time distributed parameter systems

Abstract

This paper presents an optimal discrete-time filtering algorithm using covariance information in linear distributed parameter systems. It is assumed that observation noise is a white Gaussian process. The autocovariance function of the signal, the variance of white Gaussian noise and the observed value are used in the filtering algorithm. It is an advantage that the current filtering algorithm is applied to the case where a difference equation, which generates a signal process, is unknown in linear stochastic distributed parameter systems.