Accessible Requires Authentication Published by De Gruyter October 28, 2020

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

Abdelsatar Elmezayen and Ahmed El-Rabbany


Typically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

Funding statement: This research is supported by the Government of Ontario and Ryerson University through the Ontario Trillium Scholarship.


The authors would like to thank NovAtel for providing the Waypoint Inertial Explorer (IE) software and Cansel for providing the GNSS observations for RIHI station. Additionally, the authors would like to thank the CNES Analysis Center for making the real-time GNSS orbit and clock products available. The first author would like to extend his thanks to Nader Abdelaziz, a PhD student at Ryerson University, for his help in the field trials.


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Received: 2020-06-30
Accepted: 2020-08-31
Published Online: 2020-10-28
Published in Print: 2020-11-26

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