A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in the global navigation satellite system (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture the ionospheric random structure and estimate the ionospheric parameter using data of disperse receivers to improve the accuracy. This article proposes a unifying method named ionosphere kernel estimation algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semiparametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted first to include physical correlations. In addition, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible global position system (GPS) and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential slant total electron content (dSTEC) in the root-mean-square error with respect to inverse distance weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make a robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.