15 Citations (Scopus)

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

Fingerprint

MATLAB
Kalman filters
Navigation systems
Target tracking
Computer programming languages
Communication systems
Dynamical systems

Keywords

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

ASJC Scopus subject areas

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

Cite this

An introduction to kalman filtering with MATLAB examples. / Kovvali, Narayan; Banavar, Mahesh; Spanias, Andreas.

In: Synthesis Lectures on Signal Processing, Vol. 12, 30.09.2013, p. 1-79.

Research output: Contribution to journalArticle

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