Unscented filtering and nonlinear estimation

by: SJ Julier, JK Uhlmann

Proceedings of the IEEE, Vol. 92, No. 3. (2004), pp. 401-422.

View FullText article

DOI, IEEE Explore

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

os252, jford, willie_gt

Find related articles with these CiteULike tags

estimation, filter, filtering, kalman, nonlinear, stochdgl, survey, tracking, unscented

Abstract

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.

@article{citeulike:620346, abstract = {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.}, author = {Julier, S. J. and Uhlmann, J. K. }, citeulike-article-id = {620346}, doi = {10.1109/JPROC.2003.823141}, journal = {Proceedings of the IEEE}, keywords = {filter, kalman, nonlinear, tracking, unscented}, number = {3}, pages = {401--422}, posted-at = {2007-08-21 08:41:10}, priority = {3}, title = {Unscented filtering and nonlinear estimation}, url = {http://dx.doi.org/10.1109/JPROC.2003.823141}, volume = {92}, year = {2004} }

RIS record

TY - JOUR ID - citeulike:620346 L3 - citeulike-article-id:620346 TI - Unscented filtering and nonlinear estimation JF - Proceedings of the IEEE VL - 92 IS - 3 SP - 401 EP - 422 N2 - 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. KW - filter KW - kalman KW - nonlinear KW - tracking KW - unscented AU - Julier, SJ AU - Uhlmann, JK PY - 2004/// UR - http://dx.doi.org/10.1109/JPROC.2003.823141 ER -

RIS BibTeX