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An introduction to Kalman filtering ...
~
Banavar, Mahesh K.
An introduction to Kalman filtering with MATLAB examples /
紀錄類型:
書目-語言資料,印刷品 : 單行本
正題名/作者:
An introduction to Kalman filtering with MATLAB examples // Narayan Kovvali, Mahesh Banavar, and Andreas Spanias
作者:
Kovvali, Narayan V. S. K.
其他作者:
Banavar, Mahesh K.
出版者:
[San Rafael, Calif.] :Morgan & Claypool,c2014.
面頁冊數:
ix, 71 p. :ill. ;24 cm.
標題:
Kalman filtering. -
ISBN:
9781627051392 (pbk.) :
ISBN:
1627051392 (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, Calif.] :Morgan & Claypool,c2014. - ix, 71 p. :ill. ;24 cm. - Synthesis lectures on signal processing,#121932-1236 ;. - Synthesis lectures on signal processing ;#12..
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.) :NT1036Subjects--Uniform Titles:
MATLAB.
Subjects--Topical Terms:
180315
Kalman filtering.
LC Class. No.: QA402.3 / .K685 2014
Dewey Class. No.: 629.8312
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 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.
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