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Self-calibrating filtering algorithm based on unknown input evaluator

  • Wen Xing
  • , Jian Yang
  • , Wang Li*
  • , Zihe Mao
  • , Fei Yu
  • , Shiwei Fan
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Second Institute for Wuhan Ship Development & Design
  • Qianxun Spatial Intelligence Inc.

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

Abstract

The problem of noise anomalies and unknown input interference of in-vehicle combined navigation systems in complex environments. This paper establishes a filtering model containing unknown input disturbances, designs a state self-calibration method without measuring information by combining the characteristics of the error state model, introduces a weighted sliding window to realize the identification and decoupling of the mixed disturbances, and carries out the sequential noise filtering on the obtained unknown input estimation in order to improve the operation stability of the algorithm. The results show that based on the unknown input filtering algorithm, the combined navigation system has enhanced immunity and improved the robustness of the combined SINS/GNSS navigation system.

Original languageEnglish
Title of host publication2023 10th International Forum on Electrical Engineering and Automation, IFEEA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1127-1133
Number of pages7
ISBN (Electronic)9798350309379
DOIs
StatePublished - 2023
Event10th International Forum on Electrical Engineering and Automation, IFEEA 2023 - Hybrid, Nanjing, China
Duration: 3 Nov 20235 Nov 2023

Publication series

Name2023 10th International Forum on Electrical Engineering and Automation, IFEEA 2023

Conference

Conference10th International Forum on Electrical Engineering and Automation, IFEEA 2023
Country/TerritoryChina
CityHybrid, Nanjing
Period3/11/235/11/23

Keywords

  • combinatorial navigation
  • robustness
  • unknown interference estimation
  • weighted sliding window

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