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GPS Solutions

, 23:73 | Cite as

On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact

  • Amir KhodabandehEmail author
  • Jinling Wang
  • Chris Rizos
  • Ahmed El-Mowafy
Original Article
  • 163 Downloads

Abstract

High-precision single-receiver positioning requires the provision of reliable network-derived corrections. Care must therefore be exercised to continuously check the quality of the corrections and to detect the possible presence of mis-modeled biases in the network data. In network-RTK or its state-space implementation, PPP-RTK, quality control of the solutions is executed in two separate phases: the network component and the user component. Once confidence in the network-derived solutions is declared, a subset of the solutions is sent as corrections to a single-receiver user, thereby allowing the user to separately check the integrity of his network-aided model. In such a two-step integrity monitoring procedure, an intermediate step is missing, the integrity monitoring of the corrections themselves. It is the goal of this contribution to provide a quality control procedure for GNSS parameter solutions at the correction level, and to measure the impact a missed detection bias has on the (ambiguity-resolved) user position. New detection test statistics are derived with which the single-receiver user can check the overall validity of the corrections even before applying them to his data. A small-scale network of receivers is utilized to provide numerical insights into the detectability of mis-modeled biases using the proposed detectors and to analyze the impact of such biases on the user positioning performance.

Keywords

Global navigation satellite systems (GNSS) Integrity monitoring Detection test statistics Minimal detectable bias (MDB) Kalman filtering Network-derived corrections 

Notes

Acknowledgements

This work is supported by the Australian Research Council (ARC) Project No. DP170103341.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Civil and Environmental EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.School of Earth and Planetary SciencesCurtin UniversityPerthAustralia

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