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Kalman Filters

  • Michael Paluszek
  • Stephanie Thomas
Chapter

Abstract

Understanding or controlling a physical system often requires a model of the system, that is, knowledge of the characteristics and structure of the system. A model can be a pre-defined structure or can be determined solely through data. In the case of Kalman Filtering, we create a model and use the model as a framework for learning about the system. This is part of the Control branch of our Autonomous Learning taxonomy from Chapter  1.

References

  1. 23.
    S. Sarkka. Lecture 3: Bayesian Optimal Filtering Equations and the Kalman Filter. Technical Report, Department of Biomedical Engineering and Computational Science, Aalto University School of Science, February 2011.Google Scholar
  2. 28.
    M. C. VanDyke, J. L. Schwartz, and C. D. Hall. Unscented Kalman Filtering for Spacecraft Attitude State and Parameter Estimation. Advances in Astronautical Sciences, 2005.Google Scholar

Copyright information

© Michael Paluszek and Stephanie Thomas  2019

Authors and Affiliations

  • Michael Paluszek
    • 1
  • Stephanie Thomas
    • 1
  1. 1.PlainsboroUSA

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