GFZ Global Multi-GNSS Network and Data Processing Results

Uhlemann, Maik; Gendt, G.; Ramatschi, Markus; Deng, Zhiguo

doi:10.1007/1345_2015_120

Cited by 80 publications

(46 citation statements)

References 3 publications

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“…Validation results shown by , but also analyses performed by, e.g., Prange et al (2016) and Uhlemann et al (2016), on their own MGEX products revealed that one of the most noticeable issues of the orbits of current Galileo IOV (In Orbit Validation) products are periodic orbit errors, whose amplitudes vary as a function of the elevation angle β of the Sun above the satellite orbital planes and that these orbit errors are mapped into the satellite clock corrections. demonstrated that the orbit errors with a once-per-orbit-revolution (1/rev) signature can be significantly reduced, if the ECOM1 is used together with an a priori box model.…”

Section: Introductionmentioning

confidence: 97%

CODE’s five-system orbit and clock solution—the challenges of multi-GNSS data analysis

Prange

Orliac

Dach

et al. 2016

J Geod

166

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This article describes the processing strategy and the validation results of CODE's MGEX (COM) orbit and satellite clock solution including the satellite systems GPS, GLONASS, Galileo, BeiDou, and QZSS. The validation with orbit misclosures and SLR residuals shows that the orbits of the new systems Galileo, BeiDou, and QZSS are affected by modelling deficiencies with impact on the orbit scale (e.g., antenna calibration, Earth albedo, transmitter antenna thrust). Another weakness is the attitude and solar radiation pressure (SRP) modelling of satellites moving in the orbit normal mode-which is not yet correctly considered in the COM solution. Due to these issues we consider the current state COM solution as preliminary. We, however, use the long time series of COM products for identifying the challenges and for the assessment of model improvements. The latter is demonstrated on the example of the solar radiation pressure (SRP) model, which has been replaced by a more generalized model. The SLR validation shows that the new SRP model significantly improves the orbit determination of Galileo and QZSS satellites at times when the satellite's attitude is maintained by yaw-steering. The impact of this orbit improvement is also visible in the estimated satellite clocks-demonstrating the potential use of the new

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Section: Introductionmentioning

confidence: 97%

CODE’s five-system orbit and clock solution—the challenges of multi-GNSS data analysis

Prange

Orliac

Dach

et al. 2016

J Geod

166

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“…The multi-GNSS solutions are a challenge for MGEX analysis centers due to the increasing number of satellites, new signals and observations types, and orbit modeling difficulties caused by different attitude steering modes as well as different satellites' sensitivities to perturbing forces. Currently seven MGEX Analysis Centers provide orbit and clock products of at least one new GNSS: (1) Center for Orbit Determination in Europe (CODE), Switzerland (Prange et al 2016), (2) Centre National d'Études Spatiales (CNES), France (Loyer et al 2012), (3) Deutsches GeoForschungsZentrum (GFZ), Germany (Uhlemann et al 2016), (4) Technische Universität München (TUM), Germany (Steigenberger et al 2013), (5) Wuhan University (WU), China (Guo et al 2015), (6) Japan Aero-space Exploration Agency (JAXA), Japan (Kasho 2014), and (7) European Space Agency (ESA), Europe (Springer et al 2012). Comparisons of orbit and clocks provided by different MGEX Analysis Centers can be found in Steigenberger et al (2015); Guo et al (2017), and Prange et al (2017).…”

Section: Igs Mgex Campaignmentioning

confidence: 99%

Validation of Galileo orbits using SLR with a focus on satellites launched into incorrect orbital planes

Sośnica

Prange

Kaźmierski

et al. 2017

J Geod

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The space segment of the European Global Navigation Satellite System (GNSS) Galileo consists of In-Orbit Validation (IOV) and Full Operational Capability (FOC) spacecraft. The first pair of FOC satellites was launched into an incorrect, highly eccentric orbital plane with a lower than nominal inclination angle. All Galileo satellites are equipped with satellite laser ranging (SLR) retroreflectors which allow, for example, for the assessment of the orbit quality or for the SLR-GNSS co-location in space. The number of SLR observations to Galileo satellites has been continuously increasing thanks to a series of intensive campaigns devoted to SLR tracking of GNSS satellites initiated by the International Laser Ranging Service. This paper assesses systematic effects and quality of Galileo orbits using SLR data with a main focus on Galileo satellites launched into incorrect orbits. We compare the SLR observations with respect to microwave-based Galileo orbits generated by the Center for Orbit Determination in Europe (CODE) in the framework of the International GNSS Service Multi-GNSS Experiment for the period 2014. 0-2016.5. We analyze the SLR signature effect, which is characterized by the dependency of SLR residuals with respect to various incidence angles of laser beams for stations equipped with single-photon and multiphoton detectors. Surprisingly, the CODE orbit quality of satellites in the incorrect orbital planes is not worse than that of nominal FOC and IOV orbits. The RMS of SLR residuals is even lower by 5.0 and 1.5 mm for satellites in the incorrect Finally, we found that the empirical orbit models, which were originally designed for precise orbit determination of GNSS satellites in circular orbits, provide fully appropriate results also for highly eccentric orbits with variable linear and angular velocities.

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“…During the past three decades, the global or regional GNSS (Global Navigation Satellite System) station networks have been constructed for various applications [1][2][3], and nearly thirty years' of position time series of a larger number of permanent stations have been derived using GNSS tracking data. Thanks to these position time-series data, various geophysical phenomena, such as plate tectonics [4], crustal deformation [5], post-glacial rebound [6] and vertical land motion at tide gauges [7], have been investigated more clearly.…”

Section: Introductionmentioning

confidence: 99%

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

Shen

Wang

2020

Remote Sensing

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The daily position time series derived by Global Navigation Satellite System (GNSS) contain nonlinear signals which are suitably extracted by using wavelet analysis. Considering formal errors are also provided in daily GNSS solutions, a weighted wavelet analysis is proposed in this contribution where the weight factors are constructed via the formal errors. The proposed approach is applied to process the position time series of 27 permanent stations from the Crustal Movement Observation Network of China (CMONOC), compared to traditional wavelet analysis. The results show that the proposed approach can extract more exact signals than traditional wavelet analysis, with the average error reductions are 13.24%, 13.53% and 9.35% in north, east and up coordinate components, respectively. The results from 500 simulations indicate that the signals extracted by proposed approach are closer to true signals than the traditional wavelet analysis.Remote Sens. 2020, 12, 992 2 of 16 filter (AWF) [28]. Wavelet analysis (WA) is one of the data-driven approaches to analyzing the non-linear time series. It was initially proposed by Morlet et al. [29,30] to analyze the seismic signals in geophysics and was popularized by Grossmann and Morlet [31] and Goupillaud et al. [32]. Nowadays, WA becomes a new mathematical approach and is widely applied in geodesy and geophysics [33,34], remote sensing [35,36] and hydrology [37,38]. Unlike the Fourier transform (FT) [39] which maps a time series from time domain to frequency domain, the wavelet transform (WT) can simultaneously describe the time-frequency characteristics of a time series, which is one of the most important properties of the wavelets [40]. As a time-frequency analysis method, the WT can decompose the time series into different components according to frequency, and then map the decomposed components at different scales to obtain the information of time series at different resolutions, which is named as multiresolution analysis (MRA) [41]. Due to its superior performance on signal processing, it has also been employed to analyze the GNSS position time series. For preprocessing of GNSS position time series, Wu et al. [42] proposed an improved 3σ method of outlier detection based on WA. Borghi et al. [43] introduced WT to detect discontinuity in GNSS position time series. For noise analysis, Kaczmarek and Kontny [44] identified the noise model using WA and demonstrated that the nature of noise did not depend on the method of modeling the time series. Wu et al. [45] proposed a wavelet-based hybrid approach to remove white noise and flicker noise originating from GNSS position time series. Li et al. [46] introduced a WT-based multiscale multiway principle component analysis (MSMPCA) to eliminate noise in high-rate GNSS data including high-frequency random noise, low-frequency errors and common mode error (CME). For signal analysis, Kaczmarek and Kontny [17] applied wavelet power spectrum analysis to position time series and found that annual signal is more significant than other pe...

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GFZ Global Multi-GNSS Network and Data Processing Results

Cited by 80 publications

References 3 publications

CODE’s five-system orbit and clock solution—the challenges of multi-GNSS data analysis

CODE’s five-system orbit and clock solution—the challenges of multi-GNSS data analysis

Validation of Galileo orbits using SLR with a focus on satellites launched into incorrect orbital planes

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

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