An introduction to kalman filtering with MATLAB examples

Narayan Kovvali, Mahesh Banavar, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Download Free Sample 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. Authors' Biographies

Original languageEnglish (US)
Pages (from-to)1-79
Number of pages79
JournalSynthesis Lectures on Signal Processing
Volume12
DOIs
StatePublished - Sep 30 2013

Keywords

  • Gaussian noise
  • Kalman filter
  • dynamical system
  • linearity
  • parameter estimation
  • sequential Bayesian estimation
  • state space model
  • tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

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