Ionospheric prediction algorithm and its application in low-latitude regions based on the physically constrained polynomial model

Abstract

With sufficient consideration of the ionospheric variation subject to solar activity and geomagnetic variation, a high-precision ionospheric prediction model, i.e., a physically constrained polynomial model (PCPM), was constructed by adding the Kp index, Dst index, F10.7P, R sunspot number, and other geomagnetic and solar variation indexes. Based on the data of Continuously Operating Reference System (CORS) stations in China Southern Power Grid, the ionospheric prediction results of PCPM were compared with the widely used and well-recognized seasonal enhanced product autoregressive integrated moving average (Arima) model without additional physical parameters. The prediction performance of the two models in different prediction time spans and the influence of model products on Precise Point Positioning-Real Time Kinematic (PPP-RTK) was emphatically analyzed. The experimental results demonstrated that the accuracy of PCPM is better than that of the seasonal Arima model on the first day of prediction, the prediction accuracy of the two models decreases with the increase of prediction time span, and the lowest accuracy of the seasonal Arima model on the first three days is 1.5 TECU. The Arima model outperforms the PCPM over a long-time span; in light of the overall accuracy, PCPM is more available in five southern provinces, outperforming the seasonal Arima model; the results of the PPP-RTK experiment showed that the PCPM could not only improve the velocity of ambiguity fixing but also improve the positioning accuracy after ambiguity fixing. Compared with the seasonal Arima model, the velocity of ambiguity fixing is increased by 16.7%, and the positioning accuracy after ambiguity fixing is improved by 61.0% in the E direction and 6.9% in the U direction compared with the results without ionospheric constraints.