An introduction to Kalman filtering with MATLAB examples /

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The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applicati...

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Bibliographic Details
Main Authors: Kovvali, Narayan V. S. K. (Author), Banavar, Mahesh K. (Author), Spanias, Andreas (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on signal processing ; # 12.
Subjects:
MATLAB.
Kalman filtering.
Bayesian estimation
dynamical system
Gaussian noise
Kalman filter
linearity
parameter estimation
sequential
state space model
tracking
Online Access:Abstract with links to full text
Description
Summary:The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript.
Item Description:Part of: Synthesis digital library of engineering and computer science.
Series from website.
Physical Description:1 PDF (ix, 71 pages) : illustrations.
Also available in print.
Format:Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Bibliography:Includes bibliographical references (pages 67-70).
ISBN:9781627051408
ISSN:1932-1694 ;
Access:Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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