Optimal recursive filtering using Gaussian mixture model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Kalman filter is an optimal recursive estimator of the system state in terms of minimum-mean-square error (MMSE) under linear Gaussian assumptions. The Gaussianity assumption is not satisfied in many applications, such as dynamic channel estimation in mobile communications, maneuvering target tracking and speech enhancement. In this paper, the MMSE estimator for linear, non-Gaussian problems is presented, where the Gaussian mixture model is used for non-Gaussian distributions. The resulting recursive algorithm, named as non-Gaussian Kalman filter (NGKF), is composed of several conventional Kalman filters combined in an optimal manner. The performance of the proposed NGKF, is compared to the Kalman and particle filters via simulations. It is shown that the proposed NGKF outperforms both the Kalman and particle filters.

Original languageEnglish
Title of host publication2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PublisherIEEE Computer Society
Pages399-404
Number of pages6
ISBN (Print)0780394046, 9780780394049
DOIs
StatePublished - 1 Jan 2005
Event2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France
Duration: 17 Jul 200520 Jul 2005

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2005

Conference

Conference2005 IEEE/SP 13th Workshop on Statistical Signal Processing
Country/TerritoryFrance
CityBordeaux
Period17/07/0520/07/05

Keywords

  • Dynamic state space
  • GMM
  • Kalman filter
  • MMSE
  • Particle filter
  • Sequential bayesian estimation

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