An improved attitude determination-based alignment for odometer-aided in-motion SINS

Xuan Xiao, Chao Xu, Jiaxin Liu

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

Abstract

In-motion coarse alignment is an important part for the odometer (OD)-aided strapdown inertial navigation system (SINS). In this paper, an improved attitude determination (AD) based alignment algorithm for OD aided in-motion SINS is proposed. Firstly, improved vector observations are proposed to reduce the model errors of the vector observations in the conventional AD-based alignment. Secondly, in order to address the side effects on the coarse alignment caused by faulty odometer measurement information, a linear state-space model is proposed, and then an OD measurement information detection and isolation procedure is established. The results of simulation experiments show that the improved alignment algorithm can effectively suppress the side effects due to the faulty OD measurement information, and the improved alignment algorithm has shorter convergence time and higher accuracy than the conventional AD-based alignment method.

Original languageEnglish
Title of host publicationProceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages287-292
Number of pages6
ISBN (Electronic)9781728180250
DOIs
Publication statusPublished - 27 Nov 2020
Event3rd International Conference on Unmanned Systems, ICUS 2020 - Harbin, China
Duration: 27 Nov 202028 Nov 2020

Publication series

NameProceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020

Conference

Conference3rd International Conference on Unmanned Systems, ICUS 2020
Country/TerritoryChina
CityHarbin
Period27/11/2028/11/20

Keywords

  • Attitude determination
  • In-motion coarse alignment
  • OD-aided SINS
  • Vector observations

Fingerprint

Dive into the research topics of 'An improved attitude determination-based alignment for odometer-aided in-motion SINS'. Together they form a unique fingerprint.

Cite this