Chapter 1. Kalman Filters

  1. Simon Haykin
  1. Simon Haykin

Published Online: 13 MAR 2002

DOI: 10.1002/0471221546.ch1

Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks

How to Cite

Haykin, S. (2001) Kalman Filters, in Kalman Filtering and Neural Networks (ed S. Haykin), John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221546.ch1

Editor Information

  1. Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada

Author Information

  1. Communications Research Laboratory, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1

Publication History

  1. Published Online: 13 MAR 2002
  2. Published Print: 1 OCT 2001

Book Series:

  1. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications, and Control

Book Series Editors:

  1. Simon Haykin

Series Editor Information

  1. Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada

ISBN Information

Print ISBN: 9780471369981

Online ISBN: 9780471221548

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Keywords:

  • Kalman filter;
  • optimum estates;
  • divergence phenomenon;
  • square-root filtering;
  • Rauch-Tung-Striebel smoother;
  • extended Kalman filter

Summary

The Kalman filter, rooted in the state-space formulation of linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage. In this chapter, an introductory treatment of Kalman filters is presented.