View source for NCS: Moving Horizon Estimation

{{cds270-2 header}} <!-- Generates the header, including table of contents and link back to main page -->

<!-- Enter a 1 paragraph description of the contents of the lecture.  Make sure to include any key concepts, so that the wiki search feature will pick them up
This is the template for CDS 270 lectures.  If you edit this page, you will see comments describing what goes in each section.  '''Do not edit this template.''' See [[CDS 270: Information for Lecturers]] for more information on how to create a wiki page corresponding to a lecture. -->

In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space, as well as asymmetric probability distributions, into account. MHE is dual to receeding horizon control (RHC) and also relies on optimization software. The lecture ends with a bried discussion on stability properties of MHE.

== Lecture Materials ==
<!-- Include links to materials that you used in your lecture.  At a minimum, this should include a link to your lecture presentation.  You might also include links to MATLAB scripts or other source code that students would find useful -->
<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] -->

== Reading ==
<!-- A reading list for the lecture. This will typically be 3-5 articles or book chapters that are particularly relevant to the material being presented. The reading list should be annotated to explain how the articles fit into the topic for the lecture. -->

== Additional Resources ==
<!-- Links to additional information. If there are good sources of additional information for students interested in exploring this topic further, these should go at the bottom of the page. -->