Filtering and prediction for discrete systems with unknown input using nonparametric algorithms

The paper addressed the filtering and prediction problems with using nonparametric algorithms for discrete stochastic systems with unknown input. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. The optimal properties of the explored algorithms are proved. Examples are given to illustrate the usefulness of the proposed approach.