GPS Solutions

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Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation

  • Dingjie Wang
  • Yi Dong
  • Qingsong Li
  • Zhaoyang Li
  • Jie Wu
Original Article
  • 211 Downloads

Abstract

MEMS-based integrated system of a global navigation satellite system (GNSS) and an inertial navigation system (INS) has been widely used in various navigation applications. However, such integration encounters some major limitations. On the one hand, the noisy MEMS-based INS undermines the accuracy with time during the frequently occurring GNSS outages caused by signal blockage or attenuation in certain situations such as urban canyon, tunnels, and high trees. On the other hand, the model mismatch between actual GNSS error and the assumed one would also degrade the obtained accuracy even with continuous GNSS aiding. To improve the overall performance for GNSS/MEMS-INS, better error models can be obtained using Allan variance (AV) analysis technique for modeling inertial sensor errors instead of the commonly recommended auto-regressive processes, and on the other hand, the measurement update in Kalman filter is improved using innovation filtering and AV calculation. The performance of each method and the combined algorithm is evaluated by a field test with either differential GNSS (DGNSS) or single-point positioning (SPP) as external aid. In addition to the considerable navigation enhancement brought by each method, the experimental results show the combined algorithm accomplishes overall accuracy improvements by about 18% (position), 8% (velocity), and 38% (attitude) for integration with DGNSS, and by about 15% (position), 75% (velocity), and 77% (attitude) for that with SPP, compared with corresponding traditional counterparts.

Keywords

GNSS MEMS INS Allan variance Error modeling Kalman filter 

Notes

Acknowledgements

The authors are grateful to Mr. Liansheng Meng, the senior engineer of the Flight Dynamics and Control Center, for his valuable work in system integration, and this makes the results of this paper stand the test of practice. The first author also greatly acknowledges the support provided by the GPS Center of Wuhan University. Prof. Xiaoji Niu and Dr. Qijin Chen in this center should be acknowledged, in particular, for their constructive advice and support in the test assessment.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dingjie Wang
    • 1
  • Yi Dong
    • 1
  • Qingsong Li
    • 1
  • Zhaoyang Li
    • 1
  • Jie Wu
    • 1
  1. 1.Flight Dynamics and Control Center, College of Aerospace Science and EngineeringNational University of Defense TechnologyChangshaChina

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