Design of unscented Kalman filter with noise statistic estimator

Lin Zhao, Xiao Xu Wang, Hong Xiang Xue, Quan Xi Xia

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

For the problem that the accuray of the conventional UKF declines and further diverges when the prior noise statistic is unknown or inaccurate, an unscented Kalman filter (UKF) with noise statistic estimator is designed. This UKF filtering algorithm based on maximum a posterior (MAP) estimation can estimate and correct the mean and covariance of the noise in real time while it estimates the states. The proposed UKF has the adaptive capability of dealing with variable noise statistic. The simulation results show the effectiveness of the proposed UKF filtering algorithm.

Original languageEnglish
Pages (from-to)1483-1488
Number of pages6
JournalKongzhi yu Juece/Control and Decision
Volume24
Issue number10
StatePublished - Oct 2009
Externally publishedYes

Keywords

  • Adaptive capability
  • Maximum a posterior estimation
  • Noise statistic estimator
  • Unscented Kalman filter

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