Thu, 21 Aug 2008 07:12:26 BST CiteULike: willie_gts filter http://www.citeulike.org/user/willie_gt/tag/filter CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/willie_gt/article/620346 <i>Proceedings of the IEEE, Vol. 92, No. 3. (2004), pp. 401-422.</i><br /><br />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. Unscented filtering and nonlinear estimation SJ Julier JK Uhlmann doi:10.1109/JPROC.2003.823141 Proceedings of the IEEE, Vol. 92, No. 3. (2004), pp. 401-422. 2006-05-09T10:39:00-00:00 2004 Proceedings of the IEEE 92 3 401 422 filter kalman nonlinear tracking unscented http://www.citeulike.org/user/willie_gt/article/347153 <i></i><br /><br />We describe a Markov chain Monte Carlo based particle filter that e#ectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper... An MCMC-based Particle Filter For Multiple 2005-10-10T19:21:26-00:00 filter mcmc particle tracking visual http://www.citeulike.org/user/willie_gt/article/347164 <i>(Nov 2001)</i><br /><br />In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental... The Unscented Particle Filter Rudolph van der Merwe Nando de Freitas Arnaud Doucet Eric Wan (Nov 2001) 2005-10-10T19:23:13-00:00 2001 filter particle