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An Introduction to Kalman Filtering with MATLAB Examples [electronic resource] / by Narayan Kovvali, Mahesh Banavar, Andreas Spanias.

By: Kovvali, Narayan [author.].
Contributor(s): Banavar, Mahesh [author.] | Spanias, Andreas [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Signal Processing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Edition: 1st ed. 2014.Description: IX, 71 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031025365.Subject(s): Engineering | Electrical engineering | Signal processing | Technology and Engineering | Electrical and Electronic Engineering | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Acknowledgments -- Introduction -- The Estimation Problem -- The Kalman Filter -- Extended and Decentralized Kalman Filtering -- Conclusion -- Notation -- Bibliography -- Authors' Biographies.
In: Springer Nature eBookSummary: 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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Acknowledgments -- Introduction -- The Estimation Problem -- The Kalman Filter -- Extended and Decentralized Kalman Filtering -- Conclusion -- Notation -- Bibliography -- Authors' Biographies.

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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