A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Guangle Jia
  • Yulong Huang
  • Mingming Bai
  • Yonggang Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)368-373
Journal / PublicationIFAC-PapersOnLine
Volume53
Issue number2
Publication statusPublished - 2020

Conference

Title21st IFAC World Congress 2020
PlaceGermany
CityBerlin
Period12 - 17 July 2020

Link(s)

Abstract

A novel robust Kalman filter based on Gaussian-Student's t mixture (GSTM) distribution is proposed to address the filtering problem of a linear system with non-stationary heavy-tailed measurement noise. The mixing probability is recursively estimated by using its previous estimates as prior information, and the state vector, the auxiliary parameter, the Bernoulli random variable and the mixing probability are jointly estimated utilizing the variational Bayesian method. The excellent performance of the proposed robust Kalman filter, compared with the existing state-of-the-art filters, is illustrated by a target tracking simulation results under the case of non-stationary heavy-tailed measurement noise.

Research Area(s)

  • Gaussian-Student's t mixture, Non-stationary heavy-tailed measurement noise, Robust Kalman filter, Variational Bayesian

Citation Format(s)

A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise. / Jia, Guangle; Huang, Yulong; Bai, Mingming; Zhang, Yonggang.

In: IFAC-PapersOnLine, Vol. 53, No. 2, 2020, p. 368-373.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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