Research Article
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Year 2024, Issue: 058, 113 - 121, 29.09.2024
https://doi.org/10.59313/jsr-a.1503888

Abstract

References

  • [1] B. Matviichuk, M. King, and C. Watson, “Estimating ocean tide loading displacements with GPS and GLONASS,” Solid Earth, vol. 11, no. 5, pp. 1849–1863, Oct. 2020, doi: 10.5194/se-11-1849-2020.
  • [2] M. Abbaszadeh, P. J. Clarke, and N. T. Penna, “Benefits of combining GPS and GLONASS for measuring ocean tide loading displacement,” J. Geod., vol. 94, no. 7, p. 63, Jul. 2020, doi: 10.1007/s00190-020-01393-5.
  • [3] N. T. Penna and M. P. Stewart, “Aliased tidal signatures in continuous GPS height time series,” Geophys. Res. Lett., vol. 30, no. 23, p. 2003GL018828, Dec. 2003, doi: 10.1029/2003GL018828.
  • [4] R. Zajdel, K. Kazmierski, and K. Sośnica, “Orbital Artifacts in Multi‐GNSS Precise Point Positioning Time Series,” J. Geophys. Res. Solid Earth, vol. 127, no. 2, p. e2021JB022994, Feb. 2022, doi: 10.1029/2021JB022994.
  • [5] D. Peng, Y. N. Lin, J.-C. Lee, H.-H. Su, and E. M. Hill, “Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors,” J. Geod., vol. 98, no. 1, p. 5, Jan. 2024, doi: 10.1007/s00190-023-01812-3.
  • [6] H. Ait-Lakbir, A. Santamaría-Gómez, and F. Perosanz, “Assessment of sub-daily ocean tide loading errors and mitigation of their propagation in multi-GNSS position time series,” GPS Solut., vol. 27, no. 3, p. 129, Jul. 2023, doi: 10.1007/s10291-023-01467-9.
  • [7] H. Pan, X. Xu, H. Zhang, T. Xu, and Z. Wei, “A Novel Method to Improve the Estimation of Ocean Tide Loading Displacements for K1 and K2 Components with GPS Observations,” Remote Sens., vol. 15, no. 11, p. 2846, May 2023, doi: 10.3390/rs15112846.
  • [8] H. Wang, M. Li, N. Wei, S.-C. Han, and Q. Zhao, “Improved estimation of ocean tide loading displacements using multi-GNSS kinematic and static precise point positioning,” GPS Solut., vol. 28, no. 1, p. 27, Jan. 2024, doi: 10.1007/s10291-023-01568-5.
  • [9] J. Bogusz and M. Figurski, “Residual K1 and K2 Oscillations in Precise GPS Solutions: Case Study,” Artif. Satell., vol. 46, no. 2, Jan. 2011, doi: 10.2478/v10018-011-0012-4.
  • [10] J. F. Zumberge, M. B. Heflin, D. C. Jefferson, M. M. Watkins, and F. H. Webb, “Precise point positioning for the efficient and robust analysis of GPS data from large networks,” J. Geophys. Res. Solid Earth, vol. 102, no. B3, pp. 5005–5017, Mar. 1997, doi: 10.1029/96JB03860.
  • [11] N. T. Penna, P. J. Clarke, M. S. Bos, and T. F. Baker, “Ocean tide loading displacements in western Europe: 1. Validation of kinematic GPS estimates,” J. Geophys. Res. Solid Earth, vol. 120, no. 9, pp. 6523–6539, Sep. 2015, doi: 10.1002/2015JB011882.
  • [12] C. E. Noll, “The crustal dynamics data information system: A resource to support scientific analysis using space geodesy,” Adv. Space Res., vol. 45, no. 12, pp. 1421–1440, Jun. 2010, doi: 10.1016/j.asr.2010.01.018.
  • [13] J. Geng et al., “PRIDE PPP-AR: an open-source software for GPS PPP ambiguity resolution,” GPS Solut., vol. 23, no. 4, p. 91, Oct. 2019, doi: 10.1007/s10291-019-0888-1.
  • [14] J. Geng and S. Mao, “Massive GNSS Network Analysis Without Baselines: Undifferenced Ambiguity Resolution,” J. Geophys. Res. Solid Earth, vol. 126, no. 10, p. e2020JB021558, Oct. 2021, doi: 10.1029/2020JB021558.
  • [15] J. Geng, Q. Wen, Q. Zhang, G. Li, and K. Zhang, “GNSS observable-specific phase biases for all-frequency PPP ambiguity resolution,” J. Geod., vol. 96, no. 2, p. 11, Feb. 2022, doi: 10.1007/s00190-022-01602-3.
  • [16] J. Geng, X. Chen, Y. Pan, and Q. Zhao, “A modified phase clock/bias model to improve PPP ambiguity resolution at Wuhan University,” J. Geod., vol. 93, no. 10, pp. 2053–2067, Oct. 2019, doi: 10.1007/s00190-019-01301-6.
  • [17] J. Geng, S. Yang, and J. Guo, “Assessing IGS GPS/Galileo/BDS-2/BDS-3 phase bias products with PRIDE PPP-AR,” Satell. Navig., vol. 2, no. 1, p. 17, Dec. 2021, doi: 10.1186/s43020-021-00049-9.
  • [18] J. Geng, Q. Zhang, G. Li, J. Liu, and D. Liu, “Observable-specific phase biases of Wuhan multi-GNSS experiment analysis center’s rapid satellite products,” Satell. Navig., vol. 3, no. 1, p. 23, Oct. 2022, doi: 10.1186/s43020-022-00084-0.
  • [19] W. Li, M. Kačmařík, and D. Kin, “Assessment of the Multi-GNSS PPP Performance Using Precise Products from the Wuhan Analysis Centre,” in Proceedings of conference GIS Ostrava 2021 Advances in Localization and Navigation, Vysoká škola báňská - Technická univerzita Ostrava, 2021. doi: 10.31490/9788024845050-9.
  • [20] S. Kedar, G. A. Hajj, B. D. Wilson, and M. B. Heflin, “The effect of the second order GPS ionospheric correction on receiver positions,” Geophys. Res. Lett., vol. 30, no. 16, p. 2003GL017639, Aug. 2003, doi: 10.1029/2003GL017639.
  • [21] J. Boehm, A. Niell, P. Tregoning, and H. Schuh, “Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data,” Geophys. Res. Lett., vol. 33, no. 7, p. 2005GL025546, Apr. 2006, doi: 10.1029/2005GL025546.
  • [22] G. Petit and B. Luzum, “IERS conventions (2010),” 2010.
  • [23] N. R. Lomb, “Least-squares frequency analysis of unequally spaced data,” Astrophys. Space Sci., vol. 39, no. 2, pp. 447–462, Feb. 1976, doi: 10.1007/BF00648343.
  • [24] J. D. Scargle, “Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data,” Astrophys. J., vol. 263, p. 835, Dec. 1982, doi: 10.1086/160554.
  • [25] Z. Altamimi, P. Rebischung, X. Collilieux, L. Métivier, and K. Chanard, “ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions,” J. Geod., vol. 97, no. 5, p. 47, May 2023, doi: 10.1007/s00190-023-01738-w.
  • [26] J. W. Tukey, Exploratory data analysis. in Addison-Wesley series in behavioral science. Reading (Mass.) Menlo Park (Calif.) London [etc.]: Addison-Wesley publ, 1977.
  • [27] R. A. Maronna, R. D. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, 1st ed. in Wiley Series in Probability and Statistics. Wiley, 2006. doi: 10.1002/0470010940.
  • [28] S. Hekimoglu, B. Erdogan, M. Soycan, and U. M. Durdag, “Univariate Approach for Detecting Outliers in Geodetic Networks,” J. Surv. Eng., vol. 140, no. 2, p. 04014006, May 2014, doi: 10.1061/(ASCE)SU.1943-5428.0000123.
  • [29] H. Duman, “GNSS-specific characteristic signals in power spectra of multi-GNSS coordinate time series,” Adv. Space Res., vol. 73, no. 12, pp. 5860–5875, Jun. 2024, doi: 10.1016/j.asr.2024.03.016.
  • [30] P. Wessel et al., “The Generic Mapping Tools Version 6,” Geochem. Geophys. Geosystems, vol. 20, no. 11, pp. 5556–5564, Nov. 2019, doi: 10.1029/2019GC008515.

Assessments of GPS satellite orbiting period effects on diurnal and semi-diurnal luni-solar declinations utilizing Galileo satellites

Year 2024, Issue: 058, 113 - 121, 29.09.2024
https://doi.org/10.59313/jsr-a.1503888

Abstract

Global Navigation Satellite Systems (GNSS) can observe a variety of surface deformations on Earth, including periodic oscillations at different frequencies. An example of such phenomena is ocean tide loadings (OTL), which result from the redistribution of water mass. The Global Positioning System (GPS) exhibits orbital geometry that causes its revisit and orbital periods to coincide with the diurnal and semi-diurnal luni-solar declination constituents, known as K1 and K2, respectively. Consequently, the system faces challenges in accurately estimating these periodic oscillations due to its orbital artifacts. This study aims to quantify the extent to which GPS orbital artifacts introduce periodic signals into the K1 and K2 constituents by utilizing the Galileo system and determining the most suitable positioning approach. A dataset from the International GNSS Service (IGS), spanning 40 days in 2024 and covering six stations, was analyzed. Coordinates were estimated using both kinematic positioning every 5 minutes and a 6-hour static precise point positioning (PPP) mode with a 3-hour shift. The power spectra for the east, north, and up components indicated that, on average, the GPS system contributes 52.8% to the K1 constituents and 66.3% to the K2 constituents. Despite expectations that the diurnal K1 and semi-diurnal K2 tidal constituents would be more prominent in the power spectra of the GPS comparing to that of natural signature or of other navigation system (Galileo for this study), the diurnal K1 tidal constituent appeared weak in the kinematic mode power spectra for the GPS system. These findings validate that the overlapped-static PPP mode is a more appropriate approach for estimating these periodic deformations.

References

  • [1] B. Matviichuk, M. King, and C. Watson, “Estimating ocean tide loading displacements with GPS and GLONASS,” Solid Earth, vol. 11, no. 5, pp. 1849–1863, Oct. 2020, doi: 10.5194/se-11-1849-2020.
  • [2] M. Abbaszadeh, P. J. Clarke, and N. T. Penna, “Benefits of combining GPS and GLONASS for measuring ocean tide loading displacement,” J. Geod., vol. 94, no. 7, p. 63, Jul. 2020, doi: 10.1007/s00190-020-01393-5.
  • [3] N. T. Penna and M. P. Stewart, “Aliased tidal signatures in continuous GPS height time series,” Geophys. Res. Lett., vol. 30, no. 23, p. 2003GL018828, Dec. 2003, doi: 10.1029/2003GL018828.
  • [4] R. Zajdel, K. Kazmierski, and K. Sośnica, “Orbital Artifacts in Multi‐GNSS Precise Point Positioning Time Series,” J. Geophys. Res. Solid Earth, vol. 127, no. 2, p. e2021JB022994, Feb. 2022, doi: 10.1029/2021JB022994.
  • [5] D. Peng, Y. N. Lin, J.-C. Lee, H.-H. Su, and E. M. Hill, “Multi-constellation GNSS interferometric reflectometry for tidal analysis: mitigations for K1 and K2 biases due to GPS geometrical errors,” J. Geod., vol. 98, no. 1, p. 5, Jan. 2024, doi: 10.1007/s00190-023-01812-3.
  • [6] H. Ait-Lakbir, A. Santamaría-Gómez, and F. Perosanz, “Assessment of sub-daily ocean tide loading errors and mitigation of their propagation in multi-GNSS position time series,” GPS Solut., vol. 27, no. 3, p. 129, Jul. 2023, doi: 10.1007/s10291-023-01467-9.
  • [7] H. Pan, X. Xu, H. Zhang, T. Xu, and Z. Wei, “A Novel Method to Improve the Estimation of Ocean Tide Loading Displacements for K1 and K2 Components with GPS Observations,” Remote Sens., vol. 15, no. 11, p. 2846, May 2023, doi: 10.3390/rs15112846.
  • [8] H. Wang, M. Li, N. Wei, S.-C. Han, and Q. Zhao, “Improved estimation of ocean tide loading displacements using multi-GNSS kinematic and static precise point positioning,” GPS Solut., vol. 28, no. 1, p. 27, Jan. 2024, doi: 10.1007/s10291-023-01568-5.
  • [9] J. Bogusz and M. Figurski, “Residual K1 and K2 Oscillations in Precise GPS Solutions: Case Study,” Artif. Satell., vol. 46, no. 2, Jan. 2011, doi: 10.2478/v10018-011-0012-4.
  • [10] J. F. Zumberge, M. B. Heflin, D. C. Jefferson, M. M. Watkins, and F. H. Webb, “Precise point positioning for the efficient and robust analysis of GPS data from large networks,” J. Geophys. Res. Solid Earth, vol. 102, no. B3, pp. 5005–5017, Mar. 1997, doi: 10.1029/96JB03860.
  • [11] N. T. Penna, P. J. Clarke, M. S. Bos, and T. F. Baker, “Ocean tide loading displacements in western Europe: 1. Validation of kinematic GPS estimates,” J. Geophys. Res. Solid Earth, vol. 120, no. 9, pp. 6523–6539, Sep. 2015, doi: 10.1002/2015JB011882.
  • [12] C. E. Noll, “The crustal dynamics data information system: A resource to support scientific analysis using space geodesy,” Adv. Space Res., vol. 45, no. 12, pp. 1421–1440, Jun. 2010, doi: 10.1016/j.asr.2010.01.018.
  • [13] J. Geng et al., “PRIDE PPP-AR: an open-source software for GPS PPP ambiguity resolution,” GPS Solut., vol. 23, no. 4, p. 91, Oct. 2019, doi: 10.1007/s10291-019-0888-1.
  • [14] J. Geng and S. Mao, “Massive GNSS Network Analysis Without Baselines: Undifferenced Ambiguity Resolution,” J. Geophys. Res. Solid Earth, vol. 126, no. 10, p. e2020JB021558, Oct. 2021, doi: 10.1029/2020JB021558.
  • [15] J. Geng, Q. Wen, Q. Zhang, G. Li, and K. Zhang, “GNSS observable-specific phase biases for all-frequency PPP ambiguity resolution,” J. Geod., vol. 96, no. 2, p. 11, Feb. 2022, doi: 10.1007/s00190-022-01602-3.
  • [16] J. Geng, X. Chen, Y. Pan, and Q. Zhao, “A modified phase clock/bias model to improve PPP ambiguity resolution at Wuhan University,” J. Geod., vol. 93, no. 10, pp. 2053–2067, Oct. 2019, doi: 10.1007/s00190-019-01301-6.
  • [17] J. Geng, S. Yang, and J. Guo, “Assessing IGS GPS/Galileo/BDS-2/BDS-3 phase bias products with PRIDE PPP-AR,” Satell. Navig., vol. 2, no. 1, p. 17, Dec. 2021, doi: 10.1186/s43020-021-00049-9.
  • [18] J. Geng, Q. Zhang, G. Li, J. Liu, and D. Liu, “Observable-specific phase biases of Wuhan multi-GNSS experiment analysis center’s rapid satellite products,” Satell. Navig., vol. 3, no. 1, p. 23, Oct. 2022, doi: 10.1186/s43020-022-00084-0.
  • [19] W. Li, M. Kačmařík, and D. Kin, “Assessment of the Multi-GNSS PPP Performance Using Precise Products from the Wuhan Analysis Centre,” in Proceedings of conference GIS Ostrava 2021 Advances in Localization and Navigation, Vysoká škola báňská - Technická univerzita Ostrava, 2021. doi: 10.31490/9788024845050-9.
  • [20] S. Kedar, G. A. Hajj, B. D. Wilson, and M. B. Heflin, “The effect of the second order GPS ionospheric correction on receiver positions,” Geophys. Res. Lett., vol. 30, no. 16, p. 2003GL017639, Aug. 2003, doi: 10.1029/2003GL017639.
  • [21] J. Boehm, A. Niell, P. Tregoning, and H. Schuh, “Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data,” Geophys. Res. Lett., vol. 33, no. 7, p. 2005GL025546, Apr. 2006, doi: 10.1029/2005GL025546.
  • [22] G. Petit and B. Luzum, “IERS conventions (2010),” 2010.
  • [23] N. R. Lomb, “Least-squares frequency analysis of unequally spaced data,” Astrophys. Space Sci., vol. 39, no. 2, pp. 447–462, Feb. 1976, doi: 10.1007/BF00648343.
  • [24] J. D. Scargle, “Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data,” Astrophys. J., vol. 263, p. 835, Dec. 1982, doi: 10.1086/160554.
  • [25] Z. Altamimi, P. Rebischung, X. Collilieux, L. Métivier, and K. Chanard, “ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions,” J. Geod., vol. 97, no. 5, p. 47, May 2023, doi: 10.1007/s00190-023-01738-w.
  • [26] J. W. Tukey, Exploratory data analysis. in Addison-Wesley series in behavioral science. Reading (Mass.) Menlo Park (Calif.) London [etc.]: Addison-Wesley publ, 1977.
  • [27] R. A. Maronna, R. D. Martin, and V. J. Yohai, Robust Statistics: Theory and Methods, 1st ed. in Wiley Series in Probability and Statistics. Wiley, 2006. doi: 10.1002/0470010940.
  • [28] S. Hekimoglu, B. Erdogan, M. Soycan, and U. M. Durdag, “Univariate Approach for Detecting Outliers in Geodetic Networks,” J. Surv. Eng., vol. 140, no. 2, p. 04014006, May 2014, doi: 10.1061/(ASCE)SU.1943-5428.0000123.
  • [29] H. Duman, “GNSS-specific characteristic signals in power spectra of multi-GNSS coordinate time series,” Adv. Space Res., vol. 73, no. 12, pp. 5860–5875, Jun. 2024, doi: 10.1016/j.asr.2024.03.016.
  • [30] P. Wessel et al., “The Generic Mapping Tools Version 6,” Geochem. Geophys. Geosystems, vol. 20, no. 11, pp. 5556–5564, Nov. 2019, doi: 10.1029/2019GC008515.
There are 30 citations in total.

Details

Primary Language English
Subjects Satellite-Based Positioning, Geodesy
Journal Section Research Articles
Authors

Hüseyin Duman 0000-0002-7340-7800

Publication Date September 29, 2024
Submission Date June 24, 2024
Acceptance Date July 29, 2024
Published in Issue Year 2024 Issue: 058

Cite

IEEE H. Duman, “Assessments of GPS satellite orbiting period effects on diurnal and semi-diurnal luni-solar declinations utilizing Galileo satellites”, JSR-A, no. 058, pp. 113–121, September 2024, doi: 10.59313/jsr-a.1503888.