OPTIMAL FILTERING ALGORITHM USING COVARIANCE INFORMATION IN LINEAR CONTINUOUS DISTRIBUTED-PARAMETER SYSTEMS

This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.