Abstract
Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is designed. The proposed model is based on the main factors that influence the variability of the predicted parameter on a diurnal, seasonal and long-term time-scale. Experimental results show that the GP-model, which is based on TEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. The GP-based approach performs better than the existing Neural Network-based approach in several cases.
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Agapitos, A., Konstantinidis, A., Haralambous, H., Papadopoulos, H. (2010). Evolutionary Prediction of Total Electron Content over Cyprus. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2010. IFIP Advances in Information and Communication Technology, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16239-8_50
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DOI: https://doi.org/10.1007/978-3-642-16239-8_50
Publisher Name: Springer, Berlin, Heidelberg
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