Filternet-integrated modeling for ionospheric total electron content prediction

The total electron content (TEC) is a crucial factor for quantify the particle concentration in the ionosphere, significantly impacting the performance of global navigation satellite systems. Related researches on TEC prediction are therefore essential for ionospheric modeling and its applications. Existing approaches are generally limited in computational efficiency and feature extraction capability. To address these problems, a filter-integrated TEC prediction model with a learnable frequency kernel is proposed to extract key characteristics from the frequency domain perspective. Experimental results show that under different ionospheric disturbances, the proposed model achieves approximately 18% to 40% improvement in RMSE compared to Transformer, and 10% to 14% improvement compared to iTransformer. This method demonstrates superior performance on spatiotemporal feature extraction over the self-attention mechanisms, revealing its wide prospects in the areas of real-time ionospheric modeling and positioning delay correction.