Abstract
Several state-space models for estimating a second-order stochastic process are proposed in this paper on the basis of the approximate Karhunen-Loève expansion. Properties of these models are studied and then the Kalman filtering method is applied. The accuracy of the models on the basis of two different situations, deterministic or random inputs, is studied by means of a simulation of a Brownian motion.
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Communicated by R. E. Kalman
This work was supported in part by DGICYT, Project No. PS93-0201.
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Ruiz, J.C., Valderrama, M.J. & Gutiérrez, R. Kalman filtering on approximate state-space models. J Optim Theory Appl 84, 415–431 (1995). https://doi.org/10.1007/BF02192123
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DOI: https://doi.org/10.1007/BF02192123