Difference between revisions of "NCS: Kalman Filtering"

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<!-- Sample lecture link: * [[Media:L1-1_Intro.pdf|Lecture: Networked Control Systems: Course Overview]] -->
 
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[http://www.cds.caltech.edu/~henriks/L4-4_Kalman.pdf Kalman Filtering]
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[[Media:L4-1_Kalman.pdf|Lecture: Kalman Filtering]]
  
 
== Reading ==
 
== Reading ==

Revision as of 01:03, 18 April 2006

Prev: Alice Planner Course Home Next: MHE

In this lecture, we study the Kalman filter for discrete-time linear systems. In particular, we see under what assumptions and in what senses the Kalman filter is an optimal estimator. To prove the results we use some results about conditional expectations and Gaussian probabiliy distributions. We show that the filter contains one prediction step and one correcter step that takes the most recent measurement into account. How the filter deals with sensor fusion is discussed and an example is used to illustrate the results.

Lecture Materials

Lecture: Kalman Filtering

Reading


Additional Resources

  • The Kalman Filter, G. Welch and G. Bishop. A webpage with many links on Kalman filter.

  • Optimal Filtering, B.D.O Anderson and J.B. Moore. Dover Books on Engineering, 2005. A reissue of a book from 1979. It contains a detailed mathematical presentation of filtering problems and the Kalman filter. A very good book.