Published

2020-04-01

GPS elevation fitting study based on ternary polynomial regression

Estudio de ajuste de elevación GPS basado en regresión polinómica ternaria

DOI:

https://doi.org/10.15446/esrj.v24n2.87228

Keywords:

Polynomial regression, GPS elevation fitting, Quadric surface fitting, Plane fitting, Geodetic height (en)
Regresión polinómica, Ajuste de elevación GPS, Ajuste de superficie cuadrática, Montaje en plano, Altura geodésica. (es)

Downloads

Authors

  • Jianmin Wang College of Surveying and Mapping and Geographic Sciences, Liaoning Technical University, Fuxin, 123000, China
  • Dongping Xie College of Surveying and Mapping and Geographic Sciences, Liaoning Technical University, Fuxin, 123000, China

For the traditional GPS elevation fitting method, the accuracy has not been significantly improved in recent years, and the method has become increasingly complicated. This paper proposes to insert the geodetic height ‘H’ into the calculation system and use a ternary polynomial regression function to fit the GPS elevation anomaly. The feasibility of the ternary polynomial regression method in GPS elevation fitting was verified by an example, and compared with the results of the traditional plane fitting and quadric surface fitting method, it was proved that the proposed method is suitable for terrain with large terrain fluctuations. The fitting residual error and the standard deviation are smaller, and through example calculations, it is concluded that the ternary polynomial regression method under the seven parameters has the highest fitting accuracy.

Para el método tradicional de ajuste de elevación GPS, la precisión no se ha mejorado significativamente en los últimos años, y el método se ha vuelto cada vez más complicado. Este documento propone insertar la altura geodésica "H" en el sistema de cálculo y utilizar una función de regresión polinómica ternaria para ajustar la anomalía de elevación del GPS. La viabilidad del método de regresión polinómica ternaria en el ajuste de elevación GPS se verificó mediante un ejemplo y, en comparación con los resultados del método tradicional de ajuste de plano y ajuste de superficie cuadrática, se demostró que el método propuesto es adecuado para terrenos con grandes fluctuaciones. El error residual de ajuste y la desviación estándar son menores, y a través de cálculos de ejemplo, se concluye que el método de regresión polinómica ternaria bajo los siete parámetros tiene la mayor precisión de ajuste.

References

Chen, Y., Ma, Q., Liu, R., & Mao, X. (2014). Discussion on the Relative Height Anomaly of GPS Elevation Fitting Method. Bulletin of Surveying and Mapping, (12), 67-69.

Ding, H., & Sun, J. (2013). Research on total least-squares methods for transformation of GPS elevation. Journal of Geodesy and Geodynamics, 33(03), 52-55+63.

Gong, X., Chen, Q., & Zhou, X. (2014). The Application Research of GPS Height Fitting Based on TLS Adjustment Method. Bulletin of Surveying and Mapping, (03), 6-8.

Guo, H. (2018). Transformation of GPS height and accuracy analysis based on combination model. Science of Surveying and Mapping, 43(02), 34-38.

Li, J. (2013). Research on Regional GPS Height Anomaly Fitting and Modeling Method. Kunming University of Science and Technology.

Liu, B., Guo, J., Shi, J., & Wu, D. (2016). A GPS Height Fitting Method Based on the EGM2008 Model and Terrain Correction. Geomatics and Information Science of Wuhan University, 41(04), 554-558.

Liu, Y., Zheng, N., Zhang, X., & Ge, L. (2016). Robust Weighted Total Least Squares Estimation for GPS Leveling Fitting. Journal of Geodesy and Geodynamics, 36(01), 30-34.

Li, Z., & Huang, J. (2016). GPS Measurement and Data Processing. Wuhan: Wuhan University Press, 385-386.

Ren, C., Liang, Y., Lan, L., & Pang, G. (2015). Influence of Different Combination Methods of GPS Elevation Fitting. Journal of Geodesy and Geodynamics, 35(06), 1036-1040+1045.

Shijun, L., & Li, S. (2015). GPS Height Fitting Research Considering Topographic Correction. Bulletin of Surveying and Mapping, (07), 66-67+93.

Škrekovski, R., Dimitrov, D., Zhong, J.M., Wu, H. L., & Gao, W. (2019). Remarks on multiplicative atom-bond connectivity index. IEEE Access, 7(1), 76806-76811.

Song, C., Bei, Jinzhong, & Dang, Y. (2014). Application of Principal Component Analysis in GPS Leveling Data Processing. Science of Surveying and Mapping, 39(06), 90-93.

Sun, J., Cui, X., & Guo, S. (2014). Fitting Method of Height Anomaly by Spherical Cap Harmonic Function Based on K-means Cluster Analysis. Bulletin of Surveying and Mapping, (02), 20-22.

Tang, J., Xu, T., & Long, Y. (2016). Analysis of Wavelet Function Used in the Fitting of GPS Elevation. Bulletin of Surveying and Mapping, (03), 58-60.

Wang, L., Wu, F., & Wu, L. (2016). Total least squares combination method of GPS height transformation. Surveying and Mapping Engineering, 25(04), 11-14.

Wang, L., Wu, F., & Wu, L. (2016). Total Least Squares Fitting Estimation Model for GPS Height Transformation. Geomatics and Information Science of Wuhan University, 41(09), 1259-1264.

Wu, J., & Miao, H. (2010). Ill-posed Matrix and Partial Biased Estimation Method in Measurement Data Processing. Bulletin of Surveying and Mapping, (09), 9-11.

Yang, F., & Xie, Y. (2017). Discussion on satellite positioning elevation fitting based on LSSVM. Journal of Navigation and Positioning, 5(01), 100-102+114.

Zhang, P., Yu, D., Zhang Y., & Xu, X. (2015). Research on GPS Elevation Fitting Method and Accuracy Comparison Experiment. Bulletin of Surveying and Mapping, (09), 54-56.

Zhao, H., Zhang, S., & Zhang, Q. (2011). GPS height fitting of weighted total least-squares adjustments. Journal of Geodesy and Geodynamics, 31(05), 88-90+96.

Zhao, Q., Xu, A., & Xu, Z. (2017). GPS levelling based on robust total least squares. Journal of Navigation and Positioning, 5(01), 95-99.

Zhou, C., Sun, J., & Guo, S. (2016). Height Anomaly Fitting Method Based on Mobile-Polyhedral Function. Bulletin of Surveying and Mapping, (12), 25-27+38.

How to Cite

APA

Wang, J. and Xie, D. (2020). GPS elevation fitting study based on ternary polynomial regression. Earth Sciences Research Journal, 24(2), 201–205. https://doi.org/10.15446/esrj.v24n2.87228

ACM

[1]
Wang, J. and Xie, D. 2020. GPS elevation fitting study based on ternary polynomial regression. Earth Sciences Research Journal. 24, 2 (Apr. 2020), 201–205. DOI:https://doi.org/10.15446/esrj.v24n2.87228.

ACS

(1)
Wang, J.; Xie, D. GPS elevation fitting study based on ternary polynomial regression. Earth sci. res. j. 2020, 24, 201-205.

ABNT

WANG, J.; XIE, D. GPS elevation fitting study based on ternary polynomial regression. Earth Sciences Research Journal, [S. l.], v. 24, n. 2, p. 201–205, 2020. DOI: 10.15446/esrj.v24n2.87228. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/87228. Acesso em: 19 apr. 2024.

Chicago

Wang, Jianmin, and Dongping Xie. 2020. “GPS elevation fitting study based on ternary polynomial regression”. Earth Sciences Research Journal 24 (2):201-5. https://doi.org/10.15446/esrj.v24n2.87228.

Harvard

Wang, J. and Xie, D. (2020) “GPS elevation fitting study based on ternary polynomial regression”, Earth Sciences Research Journal, 24(2), pp. 201–205. doi: 10.15446/esrj.v24n2.87228.

IEEE

[1]
J. Wang and D. Xie, “GPS elevation fitting study based on ternary polynomial regression”, Earth sci. res. j., vol. 24, no. 2, pp. 201–205, Apr. 2020.

MLA

Wang, J., and D. Xie. “GPS elevation fitting study based on ternary polynomial regression”. Earth Sciences Research Journal, vol. 24, no. 2, Apr. 2020, pp. 201-5, doi:10.15446/esrj.v24n2.87228.

Turabian

Wang, Jianmin, and Dongping Xie. “GPS elevation fitting study based on ternary polynomial regression”. Earth Sciences Research Journal 24, no. 2 (April 1, 2020): 201–205. Accessed April 19, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/87228.

Vancouver

1.
Wang J, Xie D. GPS elevation fitting study based on ternary polynomial regression. Earth sci. res. j. [Internet]. 2020 Apr. 1 [cited 2024 Apr. 19];24(2):201-5. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/87228

Download Citation

CrossRef Cited-by

CrossRef citations1

1. Timothy James McBride, Kenneth John Nixon. (2023). The impact of GPS cleaning techniques on vehicle dynamics calculations. 2023 31st Southern African Universities Power Engineering Conference (SAUPEC). , p.1. https://doi.org/10.1109/SAUPEC57889.2023.10057951.

Dimensions

PlumX

Article abstract page views

495

Downloads

Download data is not yet available.