Multi-epoch PPP-RTK corrections

temporal characteristics, pitfalls and user-impact

Journal Article (2024)
Authors

D.V. Psychas (European Space Agency (ESA))

Amir Khodabandeh (University of Melbourne)

Peter Teunissen (Curtin University of Technology, University of Melbourne, TU Delft - Mathematical Geodesy and Positioning)

Research Group
Mathematical Geodesy and Positioning
Copyright
© 2024 D.V. Psychas, A. Khodabandeh, P.J.G. Teunissen
To reference this document use:
https://doi.org/10.1007/s00190-024-01823-8
More Info
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Publication Year
2024
Language
English
Copyright
© 2024 D.V. Psychas, A. Khodabandeh, P.J.G. Teunissen
Research Group
Mathematical Geodesy and Positioning
Issue number
2
Volume number
98
DOI:
https://doi.org/10.1007/s00190-024-01823-8
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Abstract

PPP-RTK corrections, aiding GNSS users to achieve single-receiver integer ambiguity-resolved parameter solutions, are often estimated in a recursive manner by a provider. Such recursive, multi-epoch, estimation of the corrections relies on a set of S-basis parameters that are chosen by the provider so as to make the underlying measurement setup solvable. As a consequence, the provider can only estimate estimable forms of the corrections rather than the original corrections themselves. It is the goal of the present contribution to address the consequence of the corrections’ dependency on the provider’s S-basis, showcasing the characteristics of their multi-epoch solutions, thereby identifying potential pitfalls which the PPP-RTK user should avoid when evaluating such solutions. To this end, we develop a simulation platform that allows one to have full control over the properties of PPP-RTK corrections and demonstrate various misleading temporal behaviors that exist when interpreting the multi-epoch solutions of their estimable forms. The roles of the correction latency and time correlation in the multi-epoch user positioning performance are quantified, while the deviation of the user-reported positioning precision description from its user-actual counterpart is measured under a misspecified user stochastic model.

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