Chapter 7. The Unscented Kalman Filter

  1. Simon Haykin
  1. Eric A. Wan and
  2. Rudolph van der Merwe

Published Online: 13 MAR 2002

DOI: 10.1002/0471221546.ch7

Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks

How to Cite

Wan, E. A. and van der Merwe, R. (2001) The Unscented Kalman Filter, in Kalman Filtering and Neural Networks (ed S. Haykin), John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221546.ch7

Editor Information

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

Author Information

  1. Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, 19600 N.W. von Neumann Drive, Beaverton, OR 97006-1999, USA

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:

  • unscented Kalman filter;
  • optimal recursive estimation;
  • EKF;
  • UKF parameter estimation;
  • dual estimation;
  • particle filter;
  • UPF experiments

Summary

This chapter discusses the underlying assumptions and flaws in the EKF, and presents an alternative filter with performance superior to that of the EKF: the unscented Kalman filter (UKF). Three application areas of nonlinear estimation in which the EKF has been applied are covered as follows: state estimation, parameter estimation, and dual estimation. An overview of the framework for these areas is briefly reviewed.