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Asymptotic Behavior in Nonlinear Stochastic Filtering

dc.contributor.advisorBlankenship, G.L.en_US
dc.contributor.authorSaydy, L.en_US
dc.description.abstractA lower and upper bound approach on the optimal mean square error is used to study the asymptotic behavior of one dimensional nonlinear filters. Two aspects are treated: (1) The long time behavior (t Ġ. (2) The asmptotic behavior as a small parameter Ġ0. Lower and upper bounds that satisfy Riccati equations are derived and it is shown that for nonlinear systems with linear limiting systems, the Kalman filter designed for the limiting systems is asymptotically optimal in a reasonable sense. In the case of nonlinear systems with low measurement noise level, three asymptotically optimal filters are provided one of which is linear. In chapter 4, the stationary behavior of the Benes filter is investigated.en_US
dc.format.extent1319534 bytes
dc.relation.ispartofseriesISR; MS 1987-8en_US
dc.subjectSystems Integrationen_US
dc.titleAsymptotic Behavior in Nonlinear Stochastic Filteringen_US

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