{"id":2576,"date":"2010-07-09T02:55:39","date_gmt":"2010-07-09T02:55:39","guid":{"rendered":"https:\/\/blogs.mathworks.com\/pick\/2010\/07\/09\/learning-the-extended-non-linear-kalman-filter\/"},"modified":"2010-07-09T02:55:39","modified_gmt":"2010-07-09T02:55:39","slug":"learning-the-extended-non-linear-kalman-filter","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/pick\/2010\/07\/09\/learning-the-extended-non-linear-kalman-filter\/","title":{"rendered":"Learning the Extended (Non-Linear) Kalman Filter"},"content":{"rendered":"
\r\n <\/introduction>\r\n

Brett<\/a>'s Pick this week is \"Learning the Extended Kalman Filter\"<\/a>, by Yi Cao<\/a>.\r\n <\/p>\r\n

Last week<\/a> I wrote about Michael Kleder's<\/a> \"Learning the Kalman Filter\"<\/a> submission. Kalman filtering of linear systems is fairly easy; Michael's tutorial gave us great insight in that regard. The\r\n application of Kalman filters to nonlinear systems isn't quite as straighforward, and Yi's submission provides a welcome introduction.\r\n <\/p>\r\n

In his in-file example, Yi (who is currently, by the way, the second most-downloaded author on the File Exchange--our kind\r\n of people!) defines state equations for a tri-state nonlinear dynamic system, and then, from a noisy initial state, he simulates\r\n measurements of the system at 20 timesteps. His EKF function then calculates and returns, based on initial system covariance\r\n estimates\/measurements, the estimated system states:\r\n <\/p>\r\n

<\/p>\r\n

This is a nice follow-up to Michael's tutorial on the use of Kalman filtering in linear systems. Yi has a lot more to say<\/a> about this and similar subjects. He has posted both MATLAB and Simulink tutorials on Kalman filtering, and has written (and\r\n submitted) on extended Kalman filters, unscented Kalman filters, Kalman-Bucy filtering, unconstrained optimization with Kalman\r\n filters, and neural networks using Kalman filters.\r\n <\/p>\r\n

Comments?<\/a>.\r\n <\/p>