Chapter 5. Dual Extended Kalman Filter Methods

  1. Simon Haykin
  1. Eric A. Wan and
  2. Alex T. Nelson

Published Online: 13 MAR 2002

DOI: 10.1002/0471221546.ch5

Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks

How to Cite

Wan, E. A. and Nelson, A. T. (2001) Dual Extended Kalman Filter Methods, in Kalman Filtering and Neural Networks (ed S. Haykin), John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221546.ch5

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:

  • dual extended Kalman filter methods;
  • dual EKF-prediction error;
  • probabilistic perspective;
  • dual EKF variance estimation;
  • applications

Summary

This chapter presents a unified probabilistic and algorithmic framework for nonlinear dual estimation methods. Also included is a brief review of the EKF for both state and weight estimation, and an introduction of some of the complications in coupling the two. An example in noisy time-series prediction is provided. The development of a general probabilistic framework for dual estimation is detailed which allows the relating of various methods that have been presented in literature, and also provides a general algorithmic approach leading to a number of different dual EKF algorithms. Results on additional example data sets are also presented.