Unscented filtering and nonlinear estimation.
Proceedings of the IEEE
401 - 422.
The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT. © 2004 IEEE.
|Title:||Unscented filtering and nonlinear estimation|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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