Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

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Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

For nonlinear state space model involving random variables with arbitrary probability distributions, the state estimation given a sequence of observations is based on an appropriate criterion such as the minimum mean square error (MMSE). This leads to linear approximation in the state space of the e...

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Bibliographic Details
Main Authors:	Pasha, S., Tuan, H., Vo, Ba-Ngu
Format:	Journal Article
Published:	I E E E 2010
Subjects:	nonlinear model linear fractional transformation Bayesian filtering
Online Access:	http://hdl.handle.net/20.500.11937/29881

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