PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Model Based Learning of Sigma Points in Unscented Kalman Filtering
Ryan Turner and Carl Rasmussen
In: Machine Learning for Signal Processing (MLSP '10), 29 Aug 2010 - 1 Sep 2010, Kittilä, Finland.


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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:7766
Deposited By:Ryan Turner
Deposited On:17 March 2011