Difference between revisions of "NCS: Moving Horizon Estimation"

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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.
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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. -->
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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 into account, and deal with asymmetric probability distributions.
  
 
== Lecture Materials ==
 
== Lecture Materials ==

Revision as of 16:51, 19 April 2006

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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 into account, and deal with asymmetric probability distributions.

Lecture Materials

Reading

Additional Resources