Accurate Monitoring and Timely Prediction of Ionospheric Scintillation Using Support Vector Machine
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47079-0_39
Reference10 articles.
1. Jiao, Y., Hall, J. J., & Morton, Y. T. (2017). Automatic equatorial GPS amplitude scintillation detection using a machine learning algorithm. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 405–418. https://doi.org/10.1109/TAES.2017.2650758
2. Kintner, P. M., Ledvina, B. M., & De Paula, E. R. (2007). GPS and ionospheric scintillations. Space Weather, 5(9), 9003. https://doi.org/10.1029/2006SW000260
3. Lee, J., Morton, Y. T. J., Lee, J., Moon, H. S., & Seo, J. (2017). Monitoring and mitigation of Ionospheric anomalies for GNSS-based safety critical systems: A review of up-to-date signal processing techniques. IEEE Signal Processing Magazine, 34(5), 96–110. https://doi.org/10.1109/MSP.2017.2716406
4. Linty, N., Farasin, A., Favenza, A., & Dovis, F. (2019). Detection of GNSS ionospheric scintillations based on machine learning decision tree. IEEE Transactions on Aerospace and Electronic Systems, 55(1), 303–317. https://doi.org/10.1109/TAES.2018.2850385
5. Magsi, H., Ahmed, A., & Hussain, A. (2019). Real-time monitoring and logging of ionospheric scintillation and total electron content. In Sixth International Conference on Aerospace Science & Engineering ICASE 2019, Nov. 2019. https://doi.org/10.1109/ICASE48783.2019.9059198
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