Fusion predictors for continuous-time linear systems with different types of observations

New fusion predictors for continuous-time linear systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. High accuracy and computational efficiency of the fusion predictors are demonstrated on the several examples such as the damper harmonic oscillator motion with multisensor environment.

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