Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model
Main Article Content
Abstract
Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
K. Hozumi, P. Supnithi, S. Lerkvaranyu, T. Tsugawa, T. Nagatsuma, and T. Maruyama, “TEC prediction with neural network for equatorial latitude station
in Thailand,” Earth, Planets and Space, vol. 64, no. 6, pp. 473–483, Jun. 2012.
E. Yizengaw, M. B. Moldwin, D. Galvan, B. A. Iijima, A. Komjathy, and A. J. Mannucci, “Global plasmaspheric TEC and its relative contribution to GPS TEC,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 70, no. 11–12, pp. 1541–1548, Aug. 2008.
X. F. Ma, T. Maruyama, G. Ma, and T. Takeda, “Determination of GPS receiver differential biases by neural network parameter estimation method,” Radio Science, vol. 40, no. 1, Jan. 2005.
D. Sunehra, K. Satyanarayana, C. Viswanadh, and A. Sarma, “Estimation of total electron content and instrumental biases of low latitude global positioning system stations using Kalman filter,” IETE Journal of Research, vol. 56, no. 5, p. 235, 2010.
E. Sardón and N. Zarraoa, “Estimation of total electron content using GPS data: How stable are the differential satellite and receiver instrumental biases?,” Radio Science, vol. 32, no. 5, pp. 1899–1910, Sep. 1997.
G. Ma and T. Maruyama, “Derivation of TEC and estimation of instrumental biases from GEONET in Japan,” Annales Geophysicae, vol. 21, no. 10, pp. 2083–2093, Oct. 2003.
P. Kenpankho, P. Supnithi, and T. Nagatsuma,“Comparison of observed TEC values with IRI-2007 TEC and IRI-2007 TEC with optional foF2 measurements predictions at an equatorial region, Chumphon, Thailand,” Advances in Space Research, vol. 52, no. 10, pp. 1820–1826, Nov. 2013.
A. Chiablaem et al., “Estimation of the single GPS- receiver bias using the gradient descent algorithm,” 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, Thailand, 2016, pp. 1-5.
B.-K. Choi, J.-U. Park, K. Min Roh, and S.-J. Lee, “Comparison of GPS receiver DCB estimation methods using a GPS network,” Earth, Planets and Space, vol. 65, no. 7, pp. 707–711, Jul. 2013.
G. Ma, W. Gao, J. Li, Y. Chen, and H. Shen, “Estimation of GPS instrumental biases from small scale network,” Advances in Space Research, vol. 54, no. 5, pp. 871–882, Sep. 2014.
S. Sahu, R. Trivedi, R. K. Choudhary, A. Jain, and S. Jain, “Prediction of Total Electron Content (TEC) using Neural Network over Anomaly Crest Region
Bhopal,” Advances in Space Research, vol. 68, no. 7, pp. 2919–2929, Oct. 2021.
G. Sivavaraprasad, V. S. Deepika, D. SreenivasaRao, M. Ravi Kumar, and M. Sridhar, “Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station,” Geodesy and Geodynamics, vol. 11, no. 3, pp. 192– 201, May 2020.
K. Watthanasangmechai, P. Supnithi, S. Lerkvaranyu, T. Tsugawa, T. Nagatsuma, and T. Maruyama, “TEC prediction with neural network for equatorial latitude station in Thailand,” Earth, Planets and Space, vol. 64, no. 6, pp. 473–483, Jun. 2012.
X. F. Ma, T. Maruyama, G. Ma, and T. Takeda, “Determination of GPS receiver differential biases by neural network parameter estimation method,” Radio Science, vol. 40, no. 1, Jan. 2005.
H. Tuna, O. Arikan, F. Arikan, T. L. Gulyaeva, and U. Sezen, “Online user‐friendly slant total electron content computation from IRI‐Plas: IRI‐Plas‐STEC,” Space Weather, vol. 12, no. 1, pp. 64–75, Jan. 2014.
G. Ma and T. Maruyama, “Derivation of TEC and estimation of instrumental biases from GEONET in Japan,” Annales Geophysicae, vol. 21, no. 10, pp. 2083–2093, Oct. 2003.
D. R. Themens, P. T. Jayachandran, and R. B. Langley, “The nature of GPS differential receiver bias variability: An examination in the polar cap region,” Journal of Geophysical Research: Space Physics, vol. 120, no. 9, pp. 8155–8175, Sep. 2015.
International Association of Geodesy, “International GNSS Service,” Jul. 2022.
E. Grossi and M. Buscema, “Introduction to artificial neural networks,” European Journal of Gastroenterology & Hepatology, vol. 19, pp. 1046–1054, Jan. 2008.
C. D. Souza, “Neural network learning by the levenberg-marquardt algorithm with Bayesian regularization (part 1).” Available on URL:
http://crsouza. blogspot. ro/2009/11/neuralnetwork…, 2009.
P. Kim, MATLAB Deep Learning, 2017.
M. H. Gadallah, K. A. Hamid El-Sayed, and K. Hekman, “Intelligent process modelling using Feed-Forward Neural Networks,” International Journal of Manufacturing Technology and Management, vol. 19, no. 3/4, pp. 128–257, Feb. 2010.
P. Chen, H. Liu, Y. Ma, and N. Zheng, “Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers,” Advances in Space Research, vol. 65, no. 1, pp. 163–174, Jan. 2020.