Abstract: | In global navigation satellite systems (GNSS), the amplitude scintillation index (??4) and the phase scintillation index (????) indices are well-established to monitor ionospheric scintillation activity. However, these indices are not able to distinguish between the scintillation-induced propagation effects from the line-of-sight (LOS) dynamic. To circumvent this limitation, Kalman-based algorithms capable of separating the scintillation effects from the LOS dynamics have enabled the estimation of the scintillation phase and amplitude directly from the postcorrelated data. A remaining challenge of both scientific significance and practical concern is to properly classify the estimated time series among different scenarios of scintillation intensity. In this article, a time series convolutional neural network (CNN) is employed to classify the estimated amplitude and phase scintillation signal. The computer-generated scintillation data is based on the Cornell scintillation model (CSM), where different values of decorrelation time and ??4 index are used to build a multiclass dataset from which the CNN extracts the underneath features. Performance results, obtained via computer simulations, show that the proposed model can maintain a reasonable accuracy for certain levels of estimation noise and can therefore provide a temporal characterization of the estimated scintillation signal. |
Published in: |
Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) September 16 - 20, 2024 Hilton Baltimore Inner Harbor Baltimore, Maryland |
Pages: | 3019 - 3028 |
Cite this article: | Pacelli, Rubem Vasconcelos, Aragon-Angel, Angela, García, Adrià Rovira, de Almeida, Andre Lima Ferrer, Antreich, Felix, "Convolutional Neural Networks for Time Series Classification of Ionospheric Scintillation," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 3019-3028. https://doi.org/10.33012/2024.19929 |
Full Paper: |
ION Members/Non-Members: 1 Download Credit
Sign In |