Abstract
The most valuable achievements to date of the filtering theory of Chapter 8 belong to the linear theory which forms the subject of the present chapter and which is associated with the names of Kalman and Bucy. The Kalman filter (as this theory has come to be known) has a central place in our discussion of linear filtering not merely because of the fact that it is the precursor of the general nonlinear theory treated in Chapter 8 (and is still its most important special case) but because of its extensive applications in the post-Sputnik era to problems of tracking of satellites, signal detection, stochastic control, and aerospace engineering.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1980 Springer Science+Business Media New York
About this chapter
Cite this chapter
Kallianpur, G. (1980). Linear Filtering Theory. In: Stochastic Filtering Theory. Stochastic Modelling and Applied Probability, vol 13. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-6592-2_10
Download citation
DOI: https://doi.org/10.1007/978-1-4757-6592-2_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2810-8
Online ISBN: 978-1-4757-6592-2
eBook Packages: Springer Book Archive