Neural Network Prediction Model Based on Differential Localization

Abstract

The Global Navigation Satellite System-Reflectometry (GNSS-R) is affected by buildings, trees, etc. during the transmission process, which generates large errors. The traditional method is to use differential to eliminate most of the errors to improve positioning accuracy. In this paper, a neural network prediction model based on differential results is proposed, which uses the differential results X, Y and Z as the inputs of the neural network to predict the satellite position, and finally compare it with the real value. The paper uses Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) are used to establish training models and make predictions. The results show that compared with the ANN model, the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) of the RNN model are reduced by 1.54% and 3.59%, respectively; compared with the RNN model, the MAPE and RMSE of the LSTM-RNN model are reduced by 21.16% and 14.81%, respectively, which proves that the training accuracy and fit of the LSTM-RNN are better.