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Remote Sens. 2016, 8(7), 553; doi:10.3390/rs8070553

Application of Helmert Variance Component Based Adaptive Kalman Filter in Multi-GNSS PPP/INS Tightly Coupled Integration

1
School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
3
German Research Centre for Geosciences (GFZ), Telegrafenberg, Potsdam 14473, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Rosa Lasaponara, Richard Müller and Prasad S. Thenkabail
Received: 29 March 2016 / Revised: 16 June 2016 / Accepted: 25 June 2016 / Published: 29 June 2016
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
View Full-Text   |   Download PDF [3850 KB, uploaded 29 June 2016]   |  

Abstract

The integration of the Global Positioning System (GPS) and the Inertial Navigation System (INS) based on Real-time Kinematic (RTK) and Single Point Positioning (SPP) technology have been applied as a powerful approach in kinematic positioning and attitude determination. However, the accuracy of RTK and SPP based GPS/INS integration mode will degrade visibly along with the increasing user-base distance and the quality of pseudo-range. In order to overcome such weaknesses, the tightly coupled integration between GPS Precise Point Positioning (PPP) and INS was proposed recently. Because of the rapid development of the multi-constellation Global Navigation Satellite System (multi-GNSS), we introduce the multi-GNSS into the tightly coupled integration of PPP and INS in this paper. Meanwhile, in order to weaken the impacts of the GNSS observations with low quality and the inaccurate state model on the performance of the multi-GNSS PPP/INS tightly coupled integration, the Helmert variance component estimation based adaptive Kalman filter is employed in the algorithm implementation. Finally, a set of vehicle-borne GPS + BeiDou + GLONASS and Micro-Electro-Mechanical-Systems (MEMS) INS data is analyzed to evaluate the performance of such algorithm. The statistics indicate that the performance of the multi-GNSS PPP/INS tightly coupled integration can be enhanced significantly in terms of both position accuracy and convergence time. View Full-Text
Keywords: Global Navigation Satellite System (GNSS); Inertial Navigation System (INS); Precise Point Positioning (PPP); Tightly Coupled Integration (TCI); Helmert Variance Component Estimation (HVCE) Global Navigation Satellite System (GNSS); Inertial Navigation System (INS); Precise Point Positioning (PPP); Tightly Coupled Integration (TCI); Helmert Variance Component Estimation (HVCE)
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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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MDPI and ACS Style

Gao, Z.; Shen, W.; Zhang, H.; Ge, M.; Niu, X. Application of Helmert Variance Component Based Adaptive Kalman Filter in Multi-GNSS PPP/INS Tightly Coupled Integration. Remote Sens. 2016, 8, 553.

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