Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Global 4D Ionospheric STEC Prediction based on DeepONet for GNSS Rays (2404.15284v1)

Published 12 Mar 2024 in eess.SP and cs.AI

Abstract: The ionosphere is a vitally dynamic charged particle region in the Earth's upper atmosphere, playing a crucial role in applications such as radio communication and satellite navigation. The Slant Total Electron Contents (STEC) is an important parameter for characterizing wave propagation, representing the integrated electron density along the ray of radio signals passing through the ionosphere. The accurate prediction of STEC is essential for mitigating the ionospheric impact particularly on Global Navigation Satellite Systems (GNSS). In this work, we propose a high-precision STEC prediction model named DeepONet-STEC, which learns nonlinear operators to predict the 4D temporal-spatial integrated parameter for specified ground station - satellite ray path globally. As a demonstration, we validate the performance of the model based on GNSS observation data for global and US-CORS regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 hour prediction in quiet periods could achieve high accuracy using observation data by the Precise Point Positioning (PPP) with temporal resolution 30s. Under active solar magnetic storm periods, the DeepONet-STEC also demonstrated its robustness and superiority than traditional deep learning methods. This work presents a neural operator regression architecture for predicting the 4D temporal-spatial ionospheric parameter for satellite navigation system performance, which may be further extended for various space applications and beyond.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. P. M. Kintner and B. M. Ledvina, “The ionosphere, radio navigation, and global navigation satellite systems,” Advances in Space Research, vol. 35, no. 5, pp. 788–811, 2005.
  2. C. Hu, Y. Tian, X. Yang, T. Zeng, T. Long, and X. Dong, “Background ionosphere effects on geosynchronous sar focusing: Theoretical analysis and verification based on the beidou navigation satellite system (bds),” IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 9, no. 3, pp. 1143–1162, 2015.
  3. K. G. Budden, “Radio waves in the ionosphere,” Radio Waves in the Ionosphere, 2009.
  4. M. Pajares, J. Juan, J. Sanz, R. Orús Pérez, A. García-Rigo, J. Feltens, A. Komjathy, S. Schaer, and A. Krankowski, “The igs vtec maps: A reliable source of ionospheric information since 1998,” Journal of Geodesy, vol. 83, pp. 263–275, 11 2008.
  5. L. Liu, S. Zou, Y. Yao, and E. Aa, “Multi-scale ionosphere responses to the may 2017 magnetic storm over the asian sector,” GPS Solut., vol. 24, no. 1, dec 2019. [Online]. Available: https://doi.org/10.1007/s10291-019-0940-1
  6. C. Shi, S. Gu, Y. Lou, and M. Ge, “An improved approach to model ionospheric delays for single-frequency precise point positioning,” Advances in Space Research, vol. 49, no. 12, pp. 1698–1708, Jun. 2012.
  7. A. J. Mannucci, B. D. Wilson, D. N. Yuan, C. H. Ho, U. J. Lindqwister, and T. F. Runge, “A global mapping technique for GPS-derived ionospheric total electron content measurements,” Radio Sci., vol. 33, no. 3, pp. 565–582, May 1998.
  8. Z. Chen, W. Liao, H. Li, J. Wang, X. Deng, and S. Hong, “Prediction of global ionospheric tec based on deep learning,” Space Weather, vol. 20, 04 2022.
  9. Y. Sui, H. Fu, D. Wang, F. Xu, S. Feng, J. Cheng, and Y.-Q. Jin, “Sparse reconstruction of 3-d regional ionospheric tomography using data from a network of gnss reference stations,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–15, 2022.
  10. L. Sparks, J. Blanch, and N. Pandya, “Estimating ionospheric delay using kriging: 1. Methodology: ESTIMATING IONOSPHERIC DELAY USING KRIGING,” Radio Sci., vol. 46, no. 6, Dec. 2011.
  11. Z. Shi, N. Zhi, H. Fu, D. Wang, Y. Sui, Y. Zhao, S. Feng, and Y.-Q. Jin, “A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2022.
  12. T. YUAN, Y. CHEN, S. LIU, and J. GONG, “Prediction model for ionospheric total electron content based on deep learning recurrent neural networkormalsize,” Chinese Journal of Space Science, vol. 38, no. 20180105, p. 48, 2018. [Online]. Available: https://www.cjss.ac.cn/en/article/doi/10.11728/cjss2018.01.048
  13. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 11 1997. [Online]. Available: https://doi.org/10.1162/neco.1997.9.8.1735
  14. W. Sun, L. Xu, X. Huang, W. Zhang, T. Yuan, Z. Chen, and Y. Yan, “Forecasting of ionospheric vertical total electron content (tec) using lstm networks,” in 2017 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, 2017, pp. 340–344.
  15. A. Ruwali, A. J. S. Kumar, K. B. Prakash, G. Sivavaraprasad, and D. V. Ratnam, “Implementation of hybrid deep learning model (lstm-cnn) for ionospheric tec forecasting using gps data,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 6, pp. 1004–1008, 2021.
  16. L. Li, H. Liu, H. Le, J. Yuan, W. Shan, Y. Han, G. Yuan, C. Cui, and J. Wang, “Spatiotemporal prediction of ionospheric total electron content based on ed-convlstm,” Remote Sensing, vol. 15, no. 12, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/12/3064
  17. F. Zhu, N. Zhi, and H. Fu, “A data-driven forecast model of ionospheric slant total electron content based on decision trees,” in 2023 International Applied Computational Electromagnetics Society Symposium (ACES), 2023.
  18. W. Li, Z. Li, N. Wang, A. Liu, K. Zhou, H. Yuan, and A. Krankowski, “A satellite-based method for modeling ionospheric slant tec from gnss observations: algorithm and validation,” GPS Solutions, vol. 26, no. 1, p. 14, Nov 2021. [Online]. Available: https://doi.org/10.1007/s10291-021-01191-2
  19. Z. Li, N. B. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. M. Stuart, and A. Anandkumar, “Fourier neural operator for parametric partial differential equations,” CoRR, vol. abs/2010.08895, 2020. [Online]. Available: https://arxiv.org/abs/2010.08895
  20. Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar, “Neural operator: Graph kernel network for partial differential equations,” arXiv preprint arXiv:2003.03485, 2020.
  21. L. Lu, P. Jin, G. Pang, Z. Zhang, and G. E. Karniadakis, “Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators,” Nature Machine Intelligence, vol. 3, no. 3, pp. 218–229, Mar. 2021.
  22. B. Chen, C. Wang, W. Li, and H. Fu, “A hybrid Decoder-DeepONet operator regression framework for unaligned observation data,” Physics of Fluids, vol. 36, no. 2, p. 027132, 02 2024. [Online]. Available: https://doi.org/10.1063/5.0189473
  23. N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, and A. Anandkumar, “Neural operator: Learning maps between function spaces,” 2023.
  24. J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli et al., “Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators,” arXiv preprint arXiv:2202.11214, 2022.
  25. K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, “Accurate medium-range global weather forecasting with 3d neural networks,” Nature, pp. 1–6, 2023.
  26. K. Sivakrishna, D. Venkata Ratnam, and G. Sivavaraprasad, “A bidirectional deep-learning algorithm to forecast regional ionospheric tec maps,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 4531–4543, 2022.
  27. Q. Zhang, Y. Yao, J. Kong, X. Ma, and H. Zhu, “A new gnss tec neural network prediction algorithm with the data fusion of physical observation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–12, 2023.
  28. P. Xiong, C. Long, H. Zhou, X. Zhang, and X. Shen, “Gnss tec-based earthquake ionospheric perturbation detection using a novel deep learning framework,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 4248–4263, 2022.
  29. T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Statist., vol. 36, no. 3, Jun. 2008.
  30. N. Aronszajn, “Theory of reproducing kernels,” Transactions of the American Mathematical Society, vol. 68, no. 3, pp. 337–404, 1950.
  31. B. Schölkopf, R. Herbrich, and A. J. Smola, “A Generalized Representer Theorem,” in Computational Learning Theory, G. Goos, J. Hartmanis, J. van Leeuwen, D. Helmbold, and B. Williamson, Eds.   Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, vol. 2111, pp. 416–426.
  32. L.-A. Williscroft and A. W. V. Poole, “Neural networks, fof2, sunspot number and magnetic activity,” Geophysical Research Letters, vol. 23, no. 24, pp. 3659–3662, 1996. [Online]. Available: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/96GL03472
  33. I. M. Sobol, “On the distribution of points in a cube and the approximate evaluation of integrals,” USSR Computational Mathematics and Mathematical Physics, vol. 7, no. 4, pp. 86–112, 1967.
  34. Y. Yang, K. Zha, Y.-C. Chen, H. Wang, and D. Katabi, “Delving into deep imbalanced regression,” 2021.
  35. B. Nava, P. Coïsson, and S. Radicella, “A new version of the NeQuick ionosphere electron density model,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 70, no. 15, pp. 1856–1862, Dec. 2008.
  36. F. Zhou, D. Dong, W. Li, X. Jiang, J. Wickert, and H. Schuh, “GAMP: An open-source software of multi-GNSS precise point positioning using undifferenced and uncombined observations,” GPS Solut, vol. 22, no. 2, p. 33, Apr. 2018.
  37. B. Zhang, “Three methods to retrieve slant total electron content measurements from ground-based gps receivers and performance assessment,” Radio Science, vol. 51, no. 7, pp. 972–988, 2016. [Online]. Available: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015RS005916
  38. X. Ren, J. Chen, X. Li, and X. Zhang, “Ionospheric total electron content estimation using gnss carrier phase observations based on zero-difference integer ambiguity: Methodology and assessment,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 817–830, 2021.
  39. D. Psychas, S. Verhagen, X. Liu, Y. Memarzadeh, and H. Visser, “Assessment of ionospheric corrections for PPP-RTK using regional ionosphere modelling,” Measurement Science and Technology, vol. 30, no. 1, p. 014001, Jan. 2019.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets