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Sensors 2019, 19(2), 417; https://doi.org/10.3390/s19020417

Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration

1
School of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, China
2
Shanxi Provincial Key Laboratory for Resources, Environment and Disaster Monitoring, Jinzhong 030600, China
3
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
5
GNSS Research Center, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Received: 14 December 2018 / Revised: 11 January 2019 / Accepted: 13 January 2019 / Published: 21 January 2019
(This article belongs to the Section Remote Sensors)
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Abstract

Reliable and continuous navigation solutions are essential for high-accuracy location-based services. Currently, the real-time kinematic (RTK) based Global Positioning System (GPS) is widely utilized to satisfy such requirements. However, RTK’s accuracy and continuity are limited by the insufficient number of the visible satellites and the increasing length of base-lines between reference-stations and rovers. Recently, benefiting from the development of precise point positioning (PPP) and BeiDou satellite navigation systems (BDS), the issues existing in GPS RTK can be mitigated by using GPS and BDS together. However, the visible satellite number of GPS + BDS may decrease in dynamic environments. Therefore, the inertial navigation system (INS) is adopted to bridge GPS + BDS PPP solutions during signal outage periods. Meanwhile, because the quality of BDS geosynchronous Earth orbit (GEO) satellites is much lower than that of inclined geo-synchronous orbit (IGSO) satellites, the predicted observation residual based robust extended Kalman filter (R-EKF) is adopted to adjust the weight of GEO and IGSO data. In this paper, the mathematical model of the R-EKF aided GEO/IGSO/GPS PPP/INS tight integration, which uses the raw observations of GPS + BDS, is presented. Then, the influences of GEO, IGSO, INS, and R-EKF on PPP are evaluated by processing land-borne vehicle data. Results indicate that (1) both GEO and IGSO can provide accuracy improvement on GPS PPP; however, the contribution of IGSO is much more visible than that of GEO; (2) PPP’s accuracy and stability can be further improved by using INS; (3) the R-EKF is helpful to adjust the weight of GEO and IGSO in the GEO/IGSO/GPS PPP/INS tight integration and provide significantly higher positioning accuracy. View Full-Text
Keywords: robust extended Kalman filter (R-EKF); geosynchronous Earth orbit (GEO) satellites; inclined geo-synchronous orbit (IGSO) satellites; precise point positioning (PPP); inertial navigation system (INS) robust extended Kalman filter (R-EKF); geosynchronous Earth orbit (GEO) satellites; inclined geo-synchronous orbit (IGSO) satellites; precise point positioning (PPP); inertial navigation system (INS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gao, Z.; Li, Y.; Zhuang, Y.; Yang, H.; Pan, Y.; Zhang, H. Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration. Sensors 2019, 19, 417.

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