Design of Noise Covariance Adaptive Federated Filter Based on Variational Bayesian Theory

Xiao Ding, Xiuyun Meng*, Shusen Zhang

*Corresponding author for this work

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

Abstract

In order to solve the problem that the measurement noise covariance of the navigation measurement components in the integrated navigation system of unmanned aerial vehicle (UAV) in close formation flight is time-varying or difficult to detect accurately, which leads to the decline of filtering accuracy, the zero-reset federated filter is adopted. Different filter calculation periods and fusion periods are designed to fuse the navigation information with unequal intervals. Based on the idea of variational Bayesian inference in the subfilter, the real posterior distribution is approximated by a simple distribution, and the unknown measurement noise covariance is estimated adaptively. The mathematical simulation shows that the algorithm can effectively improve the relative navigation accuracy of UAV formation flight, and can better adapt to the situation of measurement noise covariance mutation.

Original languageEnglish
Title of host publication2023 4th International Conference on Computer Engineering and Application, ICCEA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages98-102
Number of pages5
ISBN (Electronic)9798350347548
DOIs
Publication statusPublished - 2023
Event4th International Conference on Computer Engineering and Application, ICCEA 2023 - Hangzhou, China
Duration: 7 Apr 20239 Apr 2023

Publication series

Name2023 4th International Conference on Computer Engineering and Application, ICCEA 2023

Conference

Conference4th International Conference on Computer Engineering and Application, ICCEA 2023
Country/TerritoryChina
CityHangzhou
Period7/04/239/04/23

Keywords

  • Asynchronous Filter
  • Federated Filter
  • Variational Bayesian Theory

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