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