Overview
- Provides a rigorous and concise introduction to Kalman filtering, now expanded and fully updated in its 5th edition
- Includes many end-of-chapters exercises, as well as a section at the end of the book with solutions and hints
- Also of interest to practitioners with a strong mathematical background who will be building Kalman filters and smoothers
- Includes supplementary material: sn.pub/extras
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Keywords
Table of contents (13 chapters)
Reviews
“Kalman filtering (KF) is a wide class of algorithms designed, in words selected from this outstanding book, ‘to obtain an optimal estimate’ of the state of a system from information in the presence of noise. … It is also written to serve as a reference for engineers … . The book has my highest recommendation, and it will reward readers for careful and iterative study of its text and well-designed exercises.” (Computing Reviews, October, 2017)
Authors and Affiliations
About the authors
Prof. Dr. Charles K. Chui, Stanford University, Stanford, CA, USA
Prof. Dr. Guanrong Chen, City Univesity Hong Kong, Kowloon, Hong Kong, PR China
Bibliographic Information
Book Title: Kalman Filtering
Book Subtitle: with Real-Time Applications
Authors: Charles K. Chui, Guanrong Chen
DOI: https://doi.org/10.1007/978-3-319-47612-4
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-47610-0Published: 29 March 2017
Softcover ISBN: 978-3-319-83780-2Published: 20 July 2018
eBook ISBN: 978-3-319-47612-4Published: 21 March 2017
Edition Number: 5
Number of Pages: XVIII, 247
Number of Illustrations: 34 b/w illustrations
Additional Information: Originally published as volume 17 in the series: Springer Series in Information Sciences
Topics: Mathematical Methods in Physics, Numerical and Computational Physics, Simulation, Economic Theory/Quantitative Economics/Mathematical Methods, Mathematical and Computational Engineering, Communications Engineering, Networks, Artificial Intelligence