Ensemble unscented Kalman filter for state inference in continuous–discrete systems

Ensemble unscented Kalman filter for state inference in continuous–discrete systems

The authors consider non-linear state filtering problem in continuous–discrete systems, where the system dynamics is modelled by a stochastic differential equation, and noisy measurements of the system are obtained at discrete time instances. A novel particle method is proposed based on sequential i...

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Bibliographic Details
Main Author: Bin Liu
Format: Article
Language:English
Published: Wiley 2014-05-01
Series:The Journal of Engineering
Subjects:
Kalman filters
nonlinear filters
stochastic processes
differential equations
discrete time filters
signal sampling
state inference
continuous-discrete systems
nonlinear state filtering problem
stochastic differential equation
noisy measurements
discrete time instances
particle method
sequential importance sampling
continuous-discrete unscented Kalman filter
CDUKF
particle filter
nonlinear systems
Online Access:http://digital-library.theiet.org/content/journals/10.1049/joe.2014.0076

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http://digital-library.theiet.org/content/journals/10.1049/joe.2014.0076

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