Processing of GNSS constellations and ground station networks using the raw observation approach

Strasser, Sebastian; Mayer‐Gürr, Torsten; Zehentner, Norbert

doi:10.1007/s00190-018-1223-2

Cited by 54 publications

(27 citation statements)

References 36 publications

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“…Since these are experimental products, they are not publicly available currently. Unlike all other satellite products relying on the IF LC, TUG products are calculated using a raw observation approach (Strasser et al 2018). Therefore, this product is suitable for fixing any linear combination, whereas all other products enable PPP-AR only for the IF LC of two specific frequencies.…”

Section: Observation Modelmentioning

confidence: 99%

PPP with integer ambiguity resolution for GPS and Galileo using satellite products from different analysis centers

Glaner

Weber

2021

GPS Solut

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Integer ambiguity resolution is the key for achieving the highest accuracy with Precise Point Positioning (PPP) and for significantly reducing the convergence time. Unfortunately, due to hardware phase biases originating from the satellites and receiver, fixing the phase ambiguities to their correct integer number is difficult in PPP. Nowadays, various institutions and analysis centers of the International GNSS Service (IGS) provide satellite products (orbits, clocks, biases) based on different strategies, which allow PPP with integer ambiguity resolution (PPP-AR) for GPS and Galileo. We present the theoretical background and practical application of the satellite products from CNES, CODE, SGG, and TUG. They are tested in combined GPS and Galileo PPP-AR solutions calculated using our in-house software raPPPid. The numerical results show that the choice of satellite product has an influence on the convergence time of the fixed solution. The satellite product of CODE performs better than the following, in the given order: SGGCODE, SGGGFZ, TUG, CNES, and SGGCNES. After the convergence period, a similar level of accuracy is achieved with all these products. With these satellite products and observations with an interval of 30 s, a mean convergence time of about 6 min to centimeter-level 2D positioning is achieved. Using high-rate observations and an observation interval of 1 s, this period can be reduced to a few minutes and, in the best case, just one minute.

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Section: Observation Modelmentioning

confidence: 99%

PPP with integer ambiguity resolution for GPS and Galileo using satellite products from different analysis centers

Glaner

Weber

2021

GPS Solut

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“…In the measurement models, the unknowns include not only the coordinate parameters, but also the time delays caused by the atmosphere and device hardware, as well as the ambiguities for phase measurement. In relative positioning, the hardware delays can be nonzero values and should be considered in multi-GNSS and GLONASS data processing, i.e., inter-system bias (ISB) [9,10,26] and inter-frequency bias (IFB) [27], respectively. The ISB and IFB of the measurements are correlated with the ambiguities and are the key problems to be solved.…”

Section: Mathematic Models Of Gnss Precise Data Processing and The DImentioning

confidence: 99%

Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation

Tian

Neitzel

2020

Mathematics

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Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias estimation. However, the mathematic model of GNSS phase bias estimation encounters the rank-deficiency problem, making bias estimation a difficult task. Combining the Monte-Carlo-based methods and GNSS data processing procedure can overcome the problem and provide fast-converging bias estimates. The variance reduction of the estimation algorithm has the potential to improve the accuracy of the estimates and is meaningful for precise and efficient PNT services. In this paper, firstly, we present the difficulty in phase bias estimation and introduce the sequential quasi-Monte Carlo (SQMC) method, then develop the SQMC-based GNSS phase bias estimation algorithm, and investigate the effects of the low-discrepancy sequence on variance reduction. Experiments with practical data show that the low-discrepancy sequence in the algorithm can significantly reduce the standard deviation of the estimates and shorten the convergence time of the filtering.

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“…The Gravity Recovery Object Oriented Programming System (GROOPS) used at IfG is a software suite for geodetic applications. Its feature set includes the determination of GNSS orbits, clocks and ground station networks (Strasser et al, 2019), static and time-variable gravity field solutions from satellite data, and regional gravity field modeling with terrestrial data. GROOPS is written in C++ and makes heavy use of low level Basic Linear Algebra Subprograms (BLAS) and LAPACK (Linear Algebra PACKage) subroutines.…”

Section: Groopsmentioning

confidence: 99%

“…As the example of several services, like the International GNSS Service (Johnston et al, 2017) and the International Laser Ranging Service (Pearlman et al, 2002), showed, combining solutions from various institutions, which are computed by different and independent software packages, leads to improved results. The COST-G initiative was formally established in 2019 and op-M. Lasser et al: Benchmark data for verifying background model implementations erationally provides state-of-the-art monthly global gravity models from the Gravity Recovery And Climate Experiment (GRACE, Tapley et al, 2004), GRACE Follow-On (Landerer et al, 2020) and Swarm (Friis-Christensen et al, 2006). COST-G is a product centre of the International Gravity Field Service (IGFS) under the umbrella of the International Association of Geodesy (IAG).…”

Section: Introductionmentioning

confidence: 99%

Benchmark data for verifying background model implementations in orbit and gravity field determination software

et al. 2020

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Abstract. In the framework of the COmbination Service for Time-variable Gravity fields (COST-G) gravity field solutions from different analysis centres are combined to provide a consolidated solution of improved quality and robustness to the user. As in many other satellite-related sciences, the correct application of background models plays a crucial role in gravity field determination. Therefore, we publish a set of data of various commonly used forces in orbit and gravity field modelling (Earth's gravity field, tides etc.) evaluated along a one day orbit arc of GRACE, together with auxiliary data to enable easy comparisons. The benchmark data is compiled with the GROOPS software by the Institute of Geodesy (IfG) at Graz University of Technology. It is intended to be used as a reference data set and provides the opportunity to test the implementation of these models at various institutions involved in orbit and gravity field determination from satellite tracking data. In view of the COST-G GRACE and GRACE Follow-On gravity field combinations, we document the outcome of the comparison of the background force models for the Bernese GNSS software from AIUB (Astronomical Institute, University of Bern), the EPOS software of the German Research Centre for Geosciences (GFZ), the GINS software, developed and maintained by the Groupe de Recherche de Géodésie Spatiale (GRGS), the GRACE-SIGMA software of the Leibniz University of Hannover (LUH) and the GRASP software also developed at LUH. We consider differences in the force modelling for GRACE (-FO) which are one order of magnitude smaller than the accelerometer noise of about 10−10 m s−2 to be negligible and formulate this as a benchmark for new analysis centres, which are interested to contribute to the COST-G initiative.

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Processing of GNSS constellations and ground station networks using the raw observation approach

Cited by 54 publications

References 36 publications

PPP with integer ambiguity resolution for GPS and Galileo using satellite products from different analysis centers

PPP with integer ambiguity resolution for GPS and Galileo using satellite products from different analysis centers

Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation

Benchmark data for verifying background model implementations in orbit and gravity field determination software

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