A new approach for filtering nonlinear systems.
(pp. pp. 1628-1632).
American Automatic Control Council: Evanston, US.
In this paper we describe a new recursive linear estimator for filtering systems with nonlinear process and observation models. This method uses a new parameterisation of the mean and covariance which can be transformed directly by the system equations to give predictions of the transformed mean and covariance. We show that this technique is more accurate and far easier to implement than an extended Kalman filter. Specifically, we present empirical results for the application of the new filter to the highly nonlinear kinematics of maneuvering vehicles.
|Title:||A new approach for filtering nonlinear systems|
|Additional information:||Conference proceedings distributed through the IEEE, Piscataway, US|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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