An introduction to Kalman filtering with MATLAB examples /
By: Kovvali, Narayan V. S. K [author.].
Contributor(s): Banavar, Mahesh K [author.] | Spanias, Andreas [author.].
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on signal processing: # 12.Publisher: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.Description: 1 PDF (ix, 71 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9781627051408.Subject(s): MATLAB | Kalman filtering | dynamical system | parameter estimation | tracking | state space model | sequential | Bayesian estimation | linearity | Gaussian noise | Kalman filterDDC classification: 629.8312 Online resources: Abstract with links to resource | Abstract with links to full text Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE524 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (pages 67-70).
1. Introduction --
2. The estimation problem -- 2.1 Background -- 2.1.1 Example: maximum-likelihood estimation in Gaussian noise -- 2.2 Linear estimation -- 2.3 The Bayesian approach to parameter estimation -- 2.3.1 Example: estimating the bias of a coin -- 2.4 Sequential Bayesian estimation -- 2.4.1 Example: the 1-D Kalman filter --
3. The Kalman filter -- 3.1 Theory -- 3.2 Implementation -- 3.2.1 Sample MATLAB code -- 3.2.2 Computational issues -- 3.3 Examples -- 3.3.1 Target tracking with radar -- 3.3.2 Channel estimation in communications systems -- 3.3.3 Recursive least squares (RLS) adaptive filtering --
4. Extended and decentralized Kalman filtering -- 4.1 Extended Kalman filter -- 4.1.1 Example: predator-prey system -- 4.2 Decentralized Kalman filtering -- 4.2.1 Example: distributed object tracking --
5. Conclusion -- Notation -- Bibliography -- Authors' biographies.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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 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.
Also available in print.
Title from PDF title page (viewed on October 16, 2013).
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