Turner, R and Rasmussen, CE (2012) Model based learning of sigma points in unscented Kalman filtering. Neurocomputing, 80. pp. 47-53. ISSN 0925-2312Full text not available from this repository.
The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially 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. © 2011 Elsevier B.V.
|Uncontrolled Keywords:||Gaussian process Global optimization Machine learning Sigma points State-space Unscented Kalman filtering|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:22|
|Last Modified:||26 Jan 2015 03:41|