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An introduction to Kalman filtering ...

Spanias, Andreas.

An introduction to Kalman filtering with MATLAB examples /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An introduction to Kalman filtering with MATLAB examples // Narayan Kovvali, Mahesh Banavar, and Andreas Spanias.
Author:	Kovvali, Narayan V. S. K.
other author:	Banavar, Mahesh K.
Published:	[San Rafael, California] :Morgan & Claypool Publishers, : c2014.,
Description:	ix, 71 p. :ill. ; : 24 cm.;
Subject:	Kalman filtering. -
ISBN:	9781627051392 (pbk.) :

An introduction to Kalman filtering with MATLAB examples /
Kovvali, Narayan V. S. K.

An introduction to Kalman filtering with MATLAB examples /Narayan Kovvali, Mahesh Banavar, and Andreas Spanias. - [San Rafael, California] :Morgan & Claypool Publishers,c2014. - ix, 71 p. :ill. ;24 cm. - Synthesis lectures on signal processing,#121932-1236 ;.

Includes bibliographical references (p. 67-70).

1. Introduction -- 2. The estimation problem -- 3. The Kalman filter -- 4. Extended and decentralized Kalman filtering -- 5. Conclusion.

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.

ISBN: 9781627051392 (pbk.) :NT1211Subjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

579841
Kalman filtering.

LC Class. No.: QA402.3 / .K685 2014

Dewey Class. No.: 629.8312

An introduction to Kalman filtering with MATLAB examples /
LDR:01812cam a2200241 a 4500 001 788872
003 OCoLC
005 20140502113732.0
008 141016s2014 caua 000 0 eng d
020 $a 9781627051392 (pbk.) : $c NT1211
020 $a 1627051392 (pbk.)
020 $z 9781627051408 (ebk.)
035 $a (OCoLC)854889937
035 $a 854889937
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050 # 4 $a QA402.3 $b .K685 2014
082 0 4 $2 23 $a 629.8312
100 1 $a Kovvali, Narayan V. S. K. $3 984848
245 1 3 $a An introduction to Kalman filtering with MATLAB examples / $c Narayan Kovvali, Mahesh Banavar, and Andreas Spanias.
260 # $a [San Rafael, California] : $c c2014. $b Morgan & Claypool Publishers,
300 $a ix, 71 p. : $b ill. ; $c 24 cm.
490 0 $a Synthesis lectures on signal processing, $v #12 $x 1932-1236 ;
504 $a Includes bibliographical references (p. 67-70).
505 0 # $a 1. Introduction -- 2. The estimation problem -- 3. The Kalman filter -- 4. Extended and decentralized Kalman filtering -- 5. Conclusion.
520 3 $a 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.
630 0 0 $a MATLAB. $3 557852
650 # 0 $a Kalman filtering. $3 579841
700 1 # $a Banavar, Mahesh K. $3 984849
700 1 # $a Spanias, Andreas. $3 984850