Unscented filtering and nonlinear estimation Export

@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}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/JPROC.2003.823141}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1271397}, 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} }

TY - JOUR ID - citeulike:620346 L3 - citeulike-article-id:620346 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. IS - 3 JF - Proceedings of the IEEE EP - 422 TI - Unscented filtering and nonlinear estimation VL - 92 SP - 401 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 -