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Acta Geodaetica et Geophysica

, Volume 53, Issue 4, pp 691–715 | Cite as

Loosely coupled GNSS/INS integration based on an auto regressive model in a data gap environment

  • Dashuai Chai
  • Guoliang ChenEmail author
  • Shengli Wang
  • Xiushan Lu
Original Study
  • 71 Downloads

Abstract

A data gap of GNSS and INS may occur when data are collected by a vehicle. To obtain the pose information when this data gap appears, we use a combined auto regressive (AR) model for the forecasting of INS data so that the Strap-down Inertial Navigation System can still work. A forward process is initially implemented to forecast INS data using an AR model, and then inverse prediction is performed. Finally, the raw INS data are determined using forward and inverse results with different weights. The measurement data are applied to this method and the commercial software Inertial Explorer 8.60 (IE). The experimental result shows that the errors from the filtered results of the IE for loosely coupled and tightly coupled approaches reach the meter level after the data of the GNSS and INS are retrieved, and the error is at the meter level for conventional loosely coupled approach. Conversely, the maximum error from the proposed method is at the decimeter level. The smoother results have also been affected for the loosely coupled and tightly coupled approach of the IE before this data gap of GNSS and INS appears. However, a centimeter-level result can still be obtained via piecewise smoothing for the proposed method. The data gaps of 5 s and 10 s for GNSS and INS are simulated. These experiments show that the maximum errors of the smoother results are 0.4374 m and 4.0443 m for the proposed algorithm and these errors are better than the results for the loosely coupled and tightly coupled approach of the IE and the conventional loosely coupled approach.

Keywords

GNSS INS AR model Integrated navigation 

Notes

Acknowledgements

We thank to Shandong University of Science and Technology and the company of Supersurs mobile surveying service for their data. This work was supported by the national Key Research and Development Program of China under Grant 2016YFB052105, the National Natural Science Foundation of China under Grant 41371423 and a Project Funded by the Science Foundation of Jiangsu Province under Grant bk20161181.

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Copyright information

© Akadémiai Kiadó 2018

Authors and Affiliations

  • Dashuai Chai
    • 1
  • Guoliang Chen
    • 1
    Email author
  • Shengli Wang
    • 2
  • Xiushan Lu
    • 2
  1. 1.School of Environment Science and Spatial InformaticsChina University of Mining and Technology (CUMT)XuzhouChina
  2. 2.Institute of Ocean EngineeringShandong University of Science and TechnologyQingdaoChina

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