The unscented Kalman filtering in extended noise environments

Authors:
Yucheng Zhou;Jiahe Xu;Yuanwei Jing;Georgi M. Dimirovski
Affiliations:
Faculty of Engineering, Department of Research, Institute of Wood Industry Chinese Academy of Forestry, Beijing, P.R. of China;Faculty of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, P.R. of China;Faculty of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, P.R. of China;Dogus University, Faculty of Engineering, Istanbul, R. of Turkey and SS Cyril and Methodius University, Faculty of FEIT, Skopje, R. of Macedonia
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

This paper introduces an extended environment for the unscented Kalman filtering that considers also the presence of additive noise on input observations in order to solve the problem of optimal estimation of noise-corrupted input and output sequences. This environment includes as sub-cases both errors-in-variables filtering and unscented Kalman filtering. The unscented Kalman filtering to the presence of additive noise on input observations is considered, and is used to solve the problem of optimal estimation of noise-corrupted input and output sequences. A Monte Carlo simulation shows that the performance of the unscented Kalman filtering technique leads to the expected minimal variance estimates.