NDLI: Extended and unscented Kalman filters for the identification of uncertainties in a process

Content Provider	IEEE Xplore Digital Library
Author	Nasri, M.T. Kinsner, W.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Manitoba, Winnipeg, MB, Canada (Nasri, M.T.; Kinsner, W.)
Abstract	This paper describes the application of extended and unscented Kalman filters for the identification of uncertainties in a process. The extended Kalman filter (EKF) is an optimal linear recursive algorithm that offers a solution to the filtering problem. The EKF is based on a first-order Taylor expansion to approximate the measurement and process models. This approach may cause the estimation process to diverge. Consequently, alternatives (e.g., the unscented Kalman filter, UKF) based on a fixed number of points to represent a Gaussian distribution have been introduced. The EKF and UKF have been applied for the identification of uncertainty in the attitude determination process for small satellites based on noisy measurements collected from Sun sensors and three-axis magnetometers. Simulation results indicate that the EKF and UKF perform equally well when small initial errors are present. However, when large errors are introduced, the UKF leads to a faster convergence and achieves a higher more accurate estimate of the state of the system.
Starting Page	182
Ending Page	188
File Size	891406
Page Count	7
File Format	PDF
e-ISBN	9781479907830
DOI	10.1109/ICCI-CC.2013.6622242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Current measurement Vectors Covariance matrices Uncertainty Estimation Noise attitude determination Extended Kalman filter unscented Kalman filter particle filters pico-satellites
Content Type	Text
Resource Type	Article

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