Turner, R and Rasmussen, CE (2010) *Model based learning of sigma points in unscented Kalman filtering.* Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. pp. 178-183.

## Abstract

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Item Type: | Article |
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Subjects: | UNSPECIFIED |

Divisions: | Div F > Computational and Biological Learning |

Depositing User: | Cron Job |

Date Deposited: | 15 Dec 2015 13:28 |

Last Modified: | 07 Feb 2016 03:23 |

DOI: | 10.1109/MLSP.2010.5589003 |