An adaptive UKF with noise statistic estimator

Lin Zhao, Xiaoxu Wang

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

20 Scopus citations

Abstract

The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In order to improve the performance of the UKF with uncertain or time-varying noise statistic, a novel adaptive UKF with noise statistic estimator is developed and applied to nonlinear joint estimation of both the states and time-varying noise statistic. This noise statistic estimator, based on maximum a posterior (MAP), makes use of the output measurement information to online update the mean and the covariance of the noise. The updated mean and covariance are further fed back into the normal UKF. As a result of using such an adaptive mechanism the robustness of conventional UKF is substantially improved with respect to the uncertain or time-varying noise statistic in the real system. Finally, the proposed adaptive UKF is demonstrated to be superior to the normal UKF through comparing the simulation results with and without the adaptive mechanism.

Original languageEnglish
Title of host publication2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
Pages614-618
Number of pages5
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009 - Xi'an, China
Duration: 25 May 200927 May 2009

Publication series

Name2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009

Conference

Conference2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009
Country/TerritoryChina
CityXi'an
Period25/05/0927/05/09

Keywords

  • Adaptive UKF
  • MAP estimation theory
  • Noise statistic estimator

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