Mikhail Moklyachuk, Maria Sidei, Filtering problem for functionals of stationary processes with missing observations, 2016 (2016), Article ID 21 (7 November 2016)

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Abstract

The problem of the mean-square optimal linear estimation of the functional $A\xi=\ \int\limits_{R^s}a(t)\xi(-t)dt,$ which depends on the unknown values of a stationary stochastic process $\xi(t)$ from observations of the process $\xi(t)+\eta(t)$ at points $t\in\mathbb{R} ^{-} \backslash S$ , $S=\bigcup\limits_{l=1}^{s}[-M_{l}-N_{l}, \, \ldots, \, -M_{l} ],$ $R^s=[0,\infty) \backslash S^{+},$ $S^{+}=\bigcup\limits_{l=1}^{s}[ M_{l}, \, \ldots, \, M_{l}+N_{l}]$ is considered. Formulas for calculating the mean-square error and the spectral characteristic of the optimal linear estimate of the functional are proposed under the condition of spectral certainty, where spectral densities of the processes $\xi(t)$ and $\eta(t)$ are exactly known. The minimax (robust) method of estimation is applied in the case where spectral densities are not known exactly, but sets of admissible spectral densities are given. Formulas that determine the least favorable spectral densities and the minimax spectral characteristics are proposed for some special sets of admissible spectral densities.

How to Cite this Article:

Mikhail Moklyachuk, Maria Sidei, Filtering problem for functionals of stationary processes with missing observations, Communication in Optimization Theory 2016 (2016), Article ID 21.