An Introduction to Kalman Filtering with MATLAB Examples by Andreas Spanias, Manesh Banavar and Narayan Nowatzki (2013, E-book)

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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

• An Introduction To Kalman Filtering With Matlab Examples available on September 17 2017 from VitalSource for itemprop="offers" target="_external" title="" itemscope itemtype="http://schema.org/Offer">40.0
  
• ISBN bar code 9781627051408 ξ2 registered April 08 2016
  
• ISBN bar code 9781627051408 ξ1 registered April 24 2015
  
• Product category is Book

• Sku # 9781627051408
  

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.

^ Andreas Spanias, Manesh Banavar, Narayan NowatzkiAn Introduction to Kalman Filtering with MATLAB Examples by Andreas Spanias, Manesh Banavar and Narayan Nowatzki (2013, E-book) ISBN 9781627051408 (revised Jul 2016)
^ An Introduction To Kalman Filtering With Matlab Examples VitalSource. ISBN 9781627051408 (revised Sep 2017)