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Licensed Unlicensed Requires Authentication Published by De Gruyter April 10, 2020

Stochastic modeling for VRS network-based GNSS RTK with residual interpolation uncertainty

Thanate Jongrujinan and Chalermchon Satirapod

Abstract

The key concept of the virtual reference station (VRS) network-based technique is to use the observables of multiple reference stations to generate the network corrections in the form of a virtual reference station at a nearby user’s location. Regarding the expected positioning accuracy, the novice GNSS data processing strategies have been adopted in the server-side functional model for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty in order to generate high-quality virtual reference stations. In addition, the realistic stochastic model also plays an important role to take account of the unmodelled error in the rover-side processing. The results of our previous study revealed that the minimum norm quadratic unbiased estimation (MINQUE) stochastic model procedure can improve baseline component accuracy and integer ambiguity reliability, however, it requires adequate epoch length in a solution to calculate the elements of the variance-covariance matrix. As a result, it may not be suitable for urban environment where the satellite signal interruptions take place frequently, therefore, the ambiguity resolution needs to be resolved within the limited epochs. In order to address this limitation, this study proposed the stochastic model based on using the residual interpolation uncertainty (RIU) as the weighting schemes. This indicator reflects the quality of network corrections for any satellite pair at a specific rover position and can be calculated on the epoch-by-epoch basis. The comparison results with the standard stochastic model indicated that the RIU-weight model produced slightly better positioning accuracy but increased significant level of the ambiguity resolution successful rate.

Acknowledgment

Department of Lands (DOL) and International GNSS Service (IGS) are gratefully acknowledged for providing GNSS data and orbital data respectively to work on this experiment.

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Received: 2020-02-17
Accepted: 2020-04-03
Published Online: 2020-04-10
Published in Print: 2020-07-26

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