Model based learning of sigma points in unscented Kalman filtering

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• 作者：Ryan Turner ; ^{rt324@cam.ac.uk}  ; [Author Vitae] ; Carl Edward Rasmussen ^{cer54@cam.ac.uk}  ; [Author Vitae]
• 关键词：Unscented Kalman filtering ; Sigma points ; State-space ; Machine learning ; Global optimization ; Gaussian process
• 刊名：Neurocomputing
• 出版年：2012
• 出版时间：15 March, 2012
• 年：2012
• 卷：80
• 期：Complete
• 页码：47-53
• 全文大小：433 K

文摘

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.