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.

About the Author

Nasser Kehtarnavaz is a Professor of Electrical Engineering at the University of Texas at Dallas. He has written four other books and numerous papers pertaining to signal and image processing, and regularly teaches undergraduate and graduate signal and image processing courses. Among his many professional activities, he is currently serving as Coeditor-in-Chief of Journal of Real-Time Image Processing , and Chair of the Dallas Chapter of the IEEE Signal Processing Society. Dr. Kehtarnavaz is a Fellow of SPIE, a Senior Member of IEEE, and a Professional Engineer. Shane Parris received his B.S. in Electrical Engineering from University of Texas at Dallas in 2013. His research interests include signal and image processing, and real-time implementation of signal and image processing algorithms. Abhishek Sehgal received his B.E. in Instrumentation Technology from Visvesvaraya Technological University in India in 2012, his M.S. and Ph.D. in Electrical Engineering at the University of Texas at Dallas in 2015 and 2019, respectively. His research interests include signal and image processing, and real-time implementation of signal and image processing algorithms.

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