
Kalman Filtering
with Real-Time Applications
Springer (Publisher)
5th Edition
Published on 29. March 2017
Book
Hardback
XVIII, 247 pages
978-3-319-47610-0 (ISBN)
Description
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help deepen the knowledge. This new edition has a new chapter on filtering communication networks and data processing, together with new exercises and new real-time applications.
Reviews / Votes
"This book is suitable for self-study as well as for use in a one-quarter or one-semester introductory course on Kalman filtering theory for upper-division undergraduate or first-year graduate to applied mathematics or engineering students." (Mikhail P. Moklyachuk, zbMath 1416.93001, 2019)"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)
More details
Edition
5th ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Edition type
Revised edition
Illustrations
34 s/w Abbildungen
XVIII, 247 p. 34 illus.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
571 gr
ISBN-13
978-3-319-47610-0 (9783319476100)
DOI
10.1007/978-3-319-47612-4
Schweitzer Classification
Other editions
Additional editions

Book
07/2018
5th Edition
Springer
€53.49
Shipment within 10-15 days

E-Book
03/2017
5th Edition
Springer
€53.49
Available for download
Previous edition

Book
12/2008
4th Edition
Springer
€64.15
Article exhausted; check for reprint
Persons
Prof. Dr. Charles K. Chui, Stanford University, Stanford, CA, USA
Prof. Dr. Guanrong Chen, City Univesity Hong Kong, Kowloon, Hong Kong, PR China
Content
Preliminaries.- Kalman Filter: An Elementary Approach.- Orthogonal Projection and Kalman Filter.- Correlated System and Measurement Noise Processes.- Colored Noise.- Limiting Kalman Filter.- Sequential and Square-Root Algorithms.- Extended Kalman Filter and System Identification.- Decoupling of Filtering Equations.- Kalman Filtering for Interval Systems.- Wavelet Kalman Filtering.- Distributed Estimation on Sensor Networks.- Notes.- Answers and Hints to Exercises.