Linear filters for discrete systems with uncertain measurements

This paper considers the problem of linear filtering with least mean-square errors by using covariance information in linear discrete-time stochastic systems with uncertain measurements. The state and observation noises are correlated noises and the state noises are not white. Recursive algorithms are proposed by employing the orthogonal projection lemma. The features of the designed filter are discussed. Simulation example illustrates the effectiveness of the algorithms.