Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm
-
Sumitra Iyer
und
Alka Mahajan
Abstract
The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.
References
[1] Sharma, S. K., Singh, A. K., Panda, S. K., & Ansari, K. (2020). GPS-derived ionospheric TEC variability with different solar indices over Saudi Arab region. Acta Astronautica, 174, 320–333.10.1016/j.actaastro.2020.05.024Suche in Google Scholar
[2] Panda, S. K., Gedam, S. S., & Jin, S. (2015). Ionospheric TEC variations at low latitude Indian region. In: Satellite Positioning-Methods, Models, and Applications. In Tech-Publisher, Rijeka, Croatia, 149–174.10.5772/59988Suche in Google Scholar
[3] Parkinson, B. W., Enge, P., Axelrad, P., & Spilker Jr, J. J. (Eds.) (1996). Global Positioning System: Theory and Applications, Volume II. American Institute of Aeronautics and Astronautics.10.2514/4.866395Suche in Google Scholar
[4] Baba, M. A. K., & Rao, D. V. M. (2014). Estimation Of global positioning system measurement errors For GAGAN applications. IOSR Journal of Electronics and Communication Engineering, 9(6), 66–79.10.9790/2834-09636679Suche in Google Scholar
[5] Yang, X., Li, J., & Zhang, S. (2014). Ionospheric correction for spaceborne single-frequency GPS based on a single-layer model. Journal of Earth System Science, 123(4), 767–778.10.1007/s12040-014-0442-zSuche in Google Scholar
[6] Dutt, V., & Gowsuddin, S. (2013). Ionospheric delay estimation using Klobuchar algorithm for single frequency GPS receivers. Journal of Advanced Research in Electronics, 2(2), 202–207.Suche in Google Scholar
[7] Jakowski, N., Heise, S., Wehrenpfennig, A., & Schlüter, S. (2001). TEC monitoring by GPS – A possible contribution to space weather monitoring. Physics and Chemistry of the Earth, Part C: Solar, Terrestrial, and Planetary Science, 26(8), 609–613.10.1016/S1464-1917(01)00056-3Suche in Google Scholar
[8] Sun, W., Xu, L., Huang, X., Zhang, W., Yuan, T., Chen, Z., & Yan, Y. (2017). Forecasting of ionospheric vertical total electron content (TEC) using LSTM networks. In: Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017, 2, 340–344.10.1109/ICMLC.2017.8108945Suche in Google Scholar
[9] Srivani, I., Siva Vara Prasad, G., & Venkata Ratnam, D. (2019). A deep learning-based approach to forecast ionospheric delays for GPS signals. IEEE Geoscience and Remote Sensing Letters, 16(8), 1180–1184.10.1109/LGRS.2019.2895112Suche in Google Scholar
[10] Sivavaraprasad, G., & Ratnam, D. V. (2017). Short-term forecasting of ionospheric total electron content over a low-latitude global navigation satellite system station. IET Radar, Sonar and Navigation, 11(8), 1309–1320.10.1049/iet-rsn.2017.0011Suche in Google Scholar
[11] Ratnam, D. V., Otsuka, Y., Sivavaraprasad, G., & Dabbakuti, J. R. K. K. (2019). Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station. Advances in Space Research, 63(9), 2848–2856.10.1016/j.asr.2018.03.024Suche in Google Scholar
[12] Sezen, U., Arikan, F., Arikan, O., Ugurlu, O., & Sadeghimorad, A. (2013). Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC. Space Weather, 11(5), 297–305.10.1002/swe.20054Suche in Google Scholar
[13] Li, Yuemeng, et al.(2019). Dynamic anomaly detection using vector autoregressive model. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham.10.1007/978-3-030-16148-4_46Suche in Google Scholar
[14] Cheng, H., Tan, P. N., Gao, J., & Scripps, J. (2006). Multistep-ahead time series prediction. In: Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. Lecture Notes in Computer Science, 3918, 765–774.10.1007/11731139_89Suche in Google Scholar
[15] Harivigneshwar, C. J., Dharmavenkatesan, K. B., Ajith, R., & Jeyanthi, R. (2019). Modeling of multivariate systems using vector autoregression (VAR). In: 2019 Innovations in Power and Advanced Computing Technologies, i-PACT 2019, 1–6.Suche in Google Scholar
[16] Patil, B. V., & Patil, P. R. (2018). An efficient DTW algorithm for online signature verification. In: 2018 International Conference on Advances in Communication and Computing Technology, ICACCT 2018, 50–54.10.1109/ICACCT.2018.8529614Suche in Google Scholar
[17] Assent, I., Wichterich, M., Krieger, R., Kremer, H., & Seidl, T. (2009). Anticipatory DTW for efficient similarity search in time-series databases. Proceedings of the VLDB Endowment, 2(1), 826–837.10.14778/1687627.1687721Suche in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm
- Non-tidal loading of the Baltic Sea in Latvian GNSS time series
- On the fast approximation of point clouds using Chebyshev polynomials
- Evaluation of quasi-geoid model based on astrogeodetic measurements: case of Latvia
- Corrigendum
- Corrigendum to: Analysis of a kinematic real-time robotic total station network for robot control
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm
- Non-tidal loading of the Baltic Sea in Latvian GNSS time series
- On the fast approximation of point clouds using Chebyshev polynomials
- Evaluation of quasi-geoid model based on astrogeodetic measurements: case of Latvia
- Corrigendum
- Corrigendum to: Analysis of a kinematic real-time robotic total station network for robot control
