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A robust filter based on Pearson type VII mixture distribution

  • Yujin Cui
  • , Huachuan Zhao*
  • , Guochen Wang
  • *Corresponding author for this work
  • Harbin Institute of Technology

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

Abstract

This paper addresses the single-target tracking problem in the presence of complex non-Gaussian noise. We propose a Variable Kurtosis Mixture Model (VKMM) filter based on variational Bayesian inference, which uses the Pearson type VII (PTVII) distribution mixtures to model unknown noise. An improved variational Bayesian method is developed to jointly optimise model parameters and system states, and the filter incorporates a matrix decomposition strategy to integrate prior information and multi-source measurements within a unified framework. Monte Carlo simulation results indicate that this method outperforms the comparison algorithms in both estimation accuracy and robustness.

Original languageEnglish
Title of host publicationProceedings - 2025 2nd International Academic Symposium on Electronic Information and Signal Processing, EISP 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11-16
Number of pages6
ISBN (Electronic)9798331550752
DOIs
StatePublished - 2025
Event2nd International Academic Symposium on Electronic Information and Signal Processing, EISP 2025 - Kuala Lumpur, Malaysia
Duration: 28 Nov 202530 Nov 2025

Publication series

NameProceedings - 2025 2nd International Academic Symposium on Electronic Information and Signal Processing, EISP 2025

Conference

Conference2nd International Academic Symposium on Electronic Information and Signal Processing, EISP 2025
Country/TerritoryMalaysia
CityKuala Lumpur
Period28/11/2530/11/25

Keywords

  • Cubature kalman filter (CKF)
  • Non-Gaussian noise
  • Pearson type VII distribution
  • variational Bayesian (VB)

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