In order to further improve the precision of high-precision real-time positioning in navigation,the paper proposed a multi-channel ConvLSTM model for predicting the global ionosphere:according to the nonlinear characteristics of the temporal and spatial variations of the global ionospheric map (GIM) and the correlation of the ionospheric total electron content (TEC) with solar and geomagnetic activities,the convolutional long short-term memory (ConvLSTM) neural network based on encoder-decoder was given with the multichannel inputs containing interplanetary three-hour index (Kp),sunspot number (SSN) and TEC;then,the ionospheric TEC and related data for 2018 to 2020 were used as a dataset to forecast GIM 1 day in advance.Results showed that the model based on multi-channel inputs would have a significant advantage in the forecasting task,and the ConvLSTM model with different inputs could outperform the 1-day prediction of GIM (C1PG) of the Center Orbit Determination Europe;moreover,the model with multi-channel inputs would perform well during geomagnetically quiet and stormy periods.
ionospheric total electron contentmultichannelconvolutional networklong short-term memory neural networkprediction
万方数据
维普
