GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station

Andrei, Constantin-Octavian; Lahtinen, Sonja; Nordman, Maaria; Näränen, Jyri; Koivula, Hannu; Poutanen, Markku; Hyyppä, Juha

doi:10.3390/rs10121937

Cited by 12 publications

(7 citation statements)

References 67 publications

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“…RTKLIB is widely used in navigation; for example, it can be used for automated machine guidance (AMG) and automated machine control (AMC) of mining vehicles [8], determination of position or trajectory of aircrafts [9], and navigation of marine vessels with IMU (Inertial measurement unit) [10]. RTKLIB’s PPP module can be applied to produce daily solutions for GPS time series analysis [11], and to evaluate the seismic waveforms and the co-seismic displacements derived by an earthquake [12]. Secondary development based on RTKLIB, GAMP utilizes the PPP algorithm to process multi-GNSS undifferenced and uncombined observations [13].…”

Section: Introductionmentioning

confidence: 99%

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

Sun

Zhou

et al. 2019

Sensors

113

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The update of the Android system and the emergence of the dual-frequency GNSS chips enable smartphones to acquire dual-frequency GNSS observations. In this paper, the GPS L1/L5 and Galileo E1/E5a dual-frequency PPP (precise point positioning) algorithm based on RTKLIB and GAMP was applied to analyze the positioning performance of the Xiaomi Mi 8 dual-frequency smartphone in static and kinematic modes. The results showed that in the static mode, the RMS position errors of the dual-frequency smartphone PPP solutions in the E, N, and U directions were 21.8 cm, 4.1 cm, and 11.0 cm, respectively, after convergence to 1 m within 102 min. The PPP of dual-frequency smartphone showed similar accuracy with geodetic receiver in single-frequency mode, while geodetic receiver in dual-frequency mode has higher accuracy. In the kinematic mode, the positioning track of the smartphone dual-frequency data had severe fluctuations, the positioning tracks derived from the smartphone and the geodetic receiver showed approximately difference of 3–5 m.

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Section: Introductionmentioning

confidence: 99%

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

Sun

Zhou

et al. 2019

Sensors

113

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“…In the North component of GOA_2 solution, power peaked at roughly 26-27 cpy, corresponding to the period of the ~14-day cycle. The 14-day cycle was observed in cGNSS timeseries previously [54]. It approximately follows the aliased period resulting from the unmodeled diurnal O1 and semidiurnal M2 tides [55].…”

Section: Noise Propertiesmentioning

confidence: 65%

“…In the North component of GOA_2 solution, power peaked at roughly 26-27 cpy, corresponding to the period of the~14-day cycle. The 14-day cycle was observed in cGNSS time-series previously [54].…”

Section: Noise Propertiesmentioning

confidence: 94%

The Noise Properties and Velocities from a Time-Series of Estonian Permanent GNSS Stations

et al. 2019

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The aim of this study was to estimate the noise properties, velocities, and their uncertainties from a time-series of selected (~9 years long) Estonian continuously operating Global Navigation Satellite System (GNSS) stations. Two software packages based on different processing methods, Gipsy–Oasis and Bernese, were used for daily coordinate calculations. Different methods and software (Tsview, Hector, and MIDAS) were used for coordinate time-series analysis. Outliers were removed using three different strategies. Six different stochastic noise models were used for trend estimation altogether with the analysis of the noise properties of the residual time-series with Hector. Obtained velocities were compared with different land uplift and glacial isostatic adjustment models (e.g., ICE-6G (VM5a), NKG2016LU, etc.). All compared solutions showed similar fit to the compared models. It was confirmed that the best fit to the time-series residuals were with the flicker noise plus white noise model (for the North and East component) and generalized Gauss–Markov model (for Up). Velocities from MIDAS, Tsview, and Hector solutions within the same time-series (Gipsy–Oasis or Bernese) agreed well but velocity uncertainties differed up to four times. The smallest uncertainties were obtained from Tsview; the MIDAS solution produced the most conservative values. Although the East and Up component velocities between Gipsy and Bernese solutions agreed well, the North component velocities were systematically shifted.

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“…Due to different measured data (such as global atmospheric pressure, ocean bottom pressure, land water reserves), different geophysical models and methods, the environmental loading displacement at the same stations obtained by the above institutions and softwares may be quite different. For example, Andrei et al (2018) calculated the 15-year coordinate time series of the Finnish Antarctic research station, namely ABOA, and compared the environmental loading products provided by GFZ, EOST and IMLS. The study found that the non-tidal oceanic loadings given by GFZ and EOST were signi cantly different.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of The Correction Effect of Different Environmental Loading Products On Global GNSS Coordinate Time Series

He¹,

Nie²,

Wu³

et al. 2021

Preprint

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The global navigation satellite system (GNSS) coordinate time series is affected by the environmental loading (including atmospheric loading (ATML), hydrological loading (HYDL), non-tidal oceanic loading (NTOL), etc.) and many organizations now provide grid products of these loadings. The temporal and spatial resolutions of these products, the loading models and data sources used are not the same, so the effect of correcting the nonlinear deformation of the GNSS coordinate time series is obviously different. This study mainly selects the three agencies, namely, School and Observatory of Earth Sciences (EOST) in France, German Research Center for Geosciences (GFZ) in Germany, and International Mass Loading Service (IMLS) in the United States, including 6 types of ATML models, 7 types of HYDL models and 5 NTOL models. The classification of these 18 environmental loading models was discussed, and the root mean square (RMS) reduction rate of the GNSS coordinate time series after environmental loading corrections (ELCs) was used to evaluate the performance differences of various models. Our results show that both the different models provided by the same organization and the same model provided by different organizations have different correction effects. Regardless of the models, it has a significant impact on the vertical coordinate time series. In order to correct the nonlinear deformation of the GNSS stations to the greatest extent, based on the above analysis, this study selects the optimal model combination of three environmental loadings as ECMWF_IB+MERRA2+ECCO1, and then explores its influence on the periodic signals in the GNSS coordinate time series. Research suggests that environmental loadings have a significant impact on the amplitude and phase of GNSS time series. Especially in the vertical direction, the largest RMS value can reach 8.42 mm. Before and after ELCs, the maximal difference of the annual amplitude and the half-annual amplitude at global 631 stations can reach 8.96 mm and 1.51 mm, respectively. Among them, 84.60% of the stations were corrected by the optimal environmental loading combination model, thus the nonlinear deformation was weakened.

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GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station

Cited by 12 publications

References 67 publications

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

The Noise Properties and Velocities from a Time-Series of Estonian Permanent GNSS Stations

Comparative Analysis of The Correction Effect of Different Environmental Loading Products On Global GNSS Coordinate Time Series

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