Abstract
The workability of Kalman filter is explored in the perspective of various stochastic noise statistics. The Gaussage is defined and applied at different levels in both Kalman filtering and particle filtering to evaluate the performance quality. Two parameters, DOM and DOD, are introduced and used for checking the consistence between the assumed stochastic noise covariance in Kalman filter and the truly existing covariance. Based on the proposed parameters, the method of online covariance adaptation is engineered, which is aimed to ultimately achieve the optimal Kalman performance quality.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Chen, K., Zhang, M., Batur, C. (2012). KF vs. PF Performance Quality Observed from Stochastic Noises Statistics and Online Covariance Self-adaptation. In: Zhang, T. (eds) Mechanical Engineering and Technology. Advances in Intelligent and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27329-2_40
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DOI: https://doi.org/10.1007/978-3-642-27329-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27328-5
Online ISBN: 978-3-642-27329-2
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