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This paper is concerned with distributed limited memory prediction forcontinuous-time linear stochastic systems with multiple sensors. A distributedfusion with the weighted sum structure is applied to the optimal local limitedmemory predictors. The distributed prediction algorithm represents the optimallinear fusion by weighting matrices under the minimum mean square criterion.The algorithm has the parallel structure and allows parallel processing ofobservations making it reliable since the rest faultless sensors can continueto the fusion estimation if some sensors occur faulty. The derivation ofequations for error cross-covariances between the local predictors is the keyof this paper. Example demonstrates effectiveness of the distributed limitedmemory predictor.

Limited Memory Prediction for Linear Systems with Different types of Observation