Error Prediction Model of Klobuchar Ionospheric Delay Based on TS Fuzzy Neural Network

The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.