Centre de documentation
scientifique

Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)  

Public [article]  inGPS solutions > vol 26 n° 3 (July 2022) . - n° 69 Titre : Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis Type de document : Article/Communication Auteurs : Jinpei Chen, Auteur ; Nan Zhi, Auteur ; Haofan Liao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse diachronique [Termes IGN] analyse spectrale [Termes IGN] apprentissage profond [Termes IGN] carte ionosphérique mondiale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction ionosphérique [Termes IGN] modèle dynamique [Termes IGN] positionnement par GNSS [Termes IGN] temps de convergence [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) The widely used GNSS correction services for high precision positioning take advantage of accurate real-time TEC forecasting based on vertical total electron content (VTEC) maps. The methods for modeling and forecasting are mainly based on overly simplified assumptions, which in principle cannot reflect the real situations due to limitations of the mathematical formulations. Therefore, these methods cannot comprehensively capture the features of ionospheric TEC in spatial–temporal series. To overcome the problems caused by such assumptions, we combine ConvLSTM (convolutional long short-term memory) with spectrum analysis. The method allows the extraction of high-resolution spatial–temporal patterns of the ionospheric VTEC maps and accelerates the convergence time of neural networks. Extensive experiments have been carried out for short- and long-term forecasting and demonstrated that the performance of our method is better than other state-of-the-art models developed for various time series analysis methods. Based on the data from global ionospheric maps (GIMs) products, the results show that the root-mean-square error (RMSE) of global VTEC forecasting by our method substantially improves for two hours intervals over the years 2015, 2016, 2017 and 2019 compared to existing methods, specifically, 20–50% reduction on 1 or 2 h forecasting in terms of RMSE. In addition, the method is sufficient to support real-time forecasting since it takes less than one second to output global forecasting solutions. With these properties, we can facilitate real-time and highly accurate ionosphere correction services beneficial to numerous GNSS correct services and positioning terminals. Numéro de notice : A2022-378 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01253-z Date de publication en ligne : 13/04/2022 En ligne : https://doi.org/10.1007/s10291-022-01253-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100638 [article]