Sequential Fusion for Multirate Multisensor Systems with Heavy-Tailed Noises and Unreliable Measurements

Liping Yan*, Chenying Di, Q. M.Jonathan Wu, Yuanqing Xia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student's t -distribution and the approximate t -filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the t -filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The t -filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm.

Original languageEnglish
Pages (from-to)523-532
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume52
Issue number1
DOIs
Publication statusPublished - 1 Jan 2022

Keywords

  • Fusion estimation
  • heavy-tailed noise
  • multirate multisensor system
  • unreliable measurements

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