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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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.
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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Michael Paluszek and Stephanie Thomas
About this chapter
Cite this chapter
Paluszek, M., Thomas, S. (2019). Kalman Filters. In: MATLAB Machine Learning Recipes. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3916-2_4
Download citation
DOI: https://doi.org/10.1007/978-1-4842-3916-2_4
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3915-5
Online ISBN: 978-1-4842-3916-2
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)