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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* [[Media:L4-1_Kalman.pdf|Lecture: Kalman Filtering]]
 
* [[Media:L4-1_Kalman.pdf|Lecture: Kalman Filtering]]
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* [[Media:Stateestim.pdf|Lecture notes: State estimation]]
  
 
== Reading ==
 
== Reading ==

Latest revision as of 04:51, 1 May 2006

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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

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.