An Introduction to Kalman Filtering with MATLAB Examples

An Introduction to Kalman Filtering with MATLAB Examples

Narayan Kovvali, Mahesh Banavar, Andreas Spanias
ISBN: 9781627051392 | PDF ISBN: 9781627051408
Copyright © 2013 | 81 Pages | Publication Date: 09/01/2013

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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.

Table of Contents

Acknowledgments
Introduction
The Estimation Problem
The Kalman Filter
Extended and Decentralized Kalman Filtering
Conclusion
Notation
Bibliography
Authors' Biographies

About the Author(s)

Narayan Kovvali, SenSIP Center, Arizona State University
Narayan Kovvali received the B.Tech. degree in electrical engineering from the Indian Institute of Technology, Kharagpur, India, in 2000, and the M.S. and Ph.D. degrees in electrical engineering from Duke University, Durham, North Carolina, in 2002 and 2005, respectively. In 2006, he joined the Department of Electrical Engineering at Arizona State University, Tempe, Arizona, as Assistant Research Scientist. He currently holds the position of Assistant Research Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. His research interests include statistical signal processing, detection, estimation, stochastic filtering and tracking, Bayesian data analysis, multi-sensor data fusion, Monte Carlo methods, and scientific computing. Dr. Kovvali is a Senior Member of the IEEE.

Mahesh Banavar, SenSIP Center, Arizona State University
Mahesh Banavar is a post-doctoral researcher in the School of Electrical, Computer and Energy Engineering at Arizona State University. He received the B.E. degree in Telecommunications Engineering from Visvesvaraya Technological University, Karnataka, India, in 2005, and the M.S. and Ph.D. degrees in Electrical Engineering from Arizona State University in 2007 and 2010, respectively. His research area is Signal Processing and Communications, and he is specifically working on Wireless Communications and Sensor Networks. He is a member of MENSA and the Eta Kappa Nu honor society.

Andreas Spanias, SenSIP Center, Arizona State University
Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the founder and director of the SenSIP Industry Consortium. His research interests are in the areas of adaptive signal processing, speech processing, and audio sensing. He and his student team developed the computer simulation software Java-DSP. He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal Processing Society in 2004.

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