Fusion Predictors for Discrete-Time Linear Systems with Multisensor Environment

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summing of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between them and the optimal predictor is discussed. High accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the damper harmonic oscillator motion with multisensor environment.