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.
- 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
- 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