An Approach for DVL-Aided SINS In-Motion Alignment Based on Observability Analysis

Peijia Liu, Bo Wang, Dongdong Hou, Kai Wang, Zhengyu Zhu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Currently, in-motion alignment for Strapdown inertial navigation system (SINS) is still a challenge, especially in underwater condition. This paper proposes an approach for DVL-aided SINS in-motion alignment from the perspective of observability. The core of the approach is an improved observability analysis method which overcomes the problems of existing methods. Based on numerical calculations, it can accurately and intuitively analyze the observability of the multi-dimensional complex navigation systems. In response to the problem that numerical calculations may introduce calculation errors, a solution based on prior knowledge of the system is presented. By utilizing the proposed observability analysis method to analyze the DVL-aided SINS in-motion alignment system, this paper has a further research on improving the alignment accuracy through observability incentives. Specifically, the factors which restrict the alignment accuracy are clarified, the reason for how the alignment accuracy is improved by trajectory design is explained, the question of whether the alignment accuracy can be further improved by more complex trajectories is studied, and the guidance for more efficient observability incentives is provided. Consequently, theoretical supports are presented to improve the alignment accuracy efficiently and purposefully. The effectiveness of the proposed approach is verified by simulations and semi-physical experiments.

Original languageEnglish
Article number9435358
Pages (from-to)17131-17143
Number of pages13
JournalIEEE Sensors Journal
Volume21
Issue number15
DOIs
Publication statusPublished - 1 Aug 2021

Keywords

  • DVL-aided
  • SINS in-motion alignment
  • SINS/DVL
  • numerical calculation
  • observability analysis
  • prior knowledge

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