High-rate clock variations of the Galileo IOV-1/2 satellites and their impact on carrier tracking by geodetic receivers

Montenbruck, Oliver; Hauschild, André; Häberling, Simon; Braun, Benjamin; Katsigianni, Georgia; Hugentobler, Urs

doi:10.1007/s10291-015-0503-z

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…they found non-zero, but time-stable, DISBs in the baseline setup with receivers of different types. Although some short-term periodic variations of the phase DISBs of the Galileo IOV-1 and IOV-2 satellites were observed (Odijk and Teunissen, 2013b;, we remark that these variations should not be attributed to the DISBs, but to a cross talk in their clock monitoring and control unit (Montenbruck et al, 2015). That the DISBs are very time-stable brings the question to the fore as to whether our earlier estimable ISBsδ G⋆ r,j , d G⋆ r,IF andd G⋆ r,j can be linked to these DISBs.…”

Section: Isb-corrected Modelsmentioning

confidence: 67%

PPP-RTK and inter-system biases: the ISB look-up table as a means to support multi-system PPP-RTK

Khodabandeh

Teunissen

2016

J Geod

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PPP-RTK has the potential of benefiting enormously from the integration of multiple GNSS/RNSS systems. However, since unaccounted inter system biases (ISBs) have a direct impact on the integer ambiguity resolution performance, the PPP-RTK network and user models need to be flexible enough to accommodate the occurrence of system-specific receiver biases. In this contribution we present such undifferenced, multi-system PPP-RTK full-rank models for both network and users. By an application of S-system theory, the multi-system estimable parameters are presented, thereby identifying how each of the three PPP-RTK components are affected by the presence of the systemspecific biases. As a result different scenarios are described of how these biases can be taken into account. To have users benefit the most, we propose the construction of an ISB look-up table. It allows users to search the table for a network receiver of their own type and select the corresponding ISBs, thus effectively realizing their own ISB-corrected user model. By applying such corrections, the user model is strengthened and the number of integer estimable user ambiguities is maximized.

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Section: Isb-corrected Modelsmentioning

confidence: 67%