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作者:

Xu, Hao (Xu, Hao.) | Pei, Fujun (Pei, Fujun.) | Jiang, Ning (Jiang, Ning.)

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EI Scopus

摘要:

In this paper, an in-motion initial alignment algorithm based on Lie group is proposed, which effectively solves the non-uniqueness of the unit-quaternion attitude description and the nonlinear problem when using unit-quaternion for state estimation. The algorithm divides the attitude matrix into three successive special orthogonal matrices according to the property of the rotation on Lie group description and the basic principle of inertial navigation, and isolates the angular velocity and linear velocity of the carrier under dynamic conditions. Based on the mapping relation between Lie group and Lie algebra and the optimal estimation principle, and the uncertainty of measurement noise in modeling is analyzed, an adaptive Lie group filter method is proposed to estimate the initial inertial matrix directly. It can be seen from the experimental results that compare with the unit quaternion algorithm proposed algorithm has improved the alignment accuracy and stability. This method is an excellent situation to the SINS initial alignment in-motion. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Adaptive filtering Air navigation Indium compounds Inertial navigation systems Lie groups Matrix algebra Navigation Uncertainty analysis

作者机构:

  • [ 1 ] [Xu, Hao]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Xu, Hao]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Pei, Fujun]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 4 ] [Pei, Fujun]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 5 ] [Jiang, Ning]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 6 ] [Jiang, Ning]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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ISSN: 1934-1768

年份: 2018

卷: 2018-July

页码: 4581-4586

语种: 英文

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WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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