Estimation Algorithm of Ionospheric Delay Correction Parameters Based on Neural Network

Abstract

Aiming at the difficulty of separating the ionospheric delay error in the user pseudoring observations of a single-frequency receiver, this paper proposes an error separation method based on BP neural network. The receiver’s observation data for a continuous week is used as a training sample, and the time in the sample is the satellite altitude, azimuth and pseudoring observations are used as the input unit of the BP neural network. The high-precision GIM provided by CODE is used to calculate the corresponding ionospheric delay error, and it is used as the output unit to train the BP neural network. At the end of the thesis, the single-frequency PPP model that uses parameter estimation in the ionosphere is discussed. Finally, the positioning accuracy that this model can achieve is verified through actual calculation examples.