Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

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

Internet

http://hdl.handle.net/20.500.11937/29881

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