Information Science and Engineering
Pingping QU, Tianfeng LIU, Ershen WANG, Zibo YUAN, Jian YANG, Da LIU, Wei SHI, Wanying DUAN
In order to improve the spoofing interference detection capability of satellite navigation system, a satellite navigation spoofing interference detection algorithm based on RNN was investigated, and the loss function was designed. In order to improve the accuracy of data prediction, a data preprocessing method was studied, which maped the data in a fixed interval and amplifies the characteristics of the data. The experimental results show that the prediction accuracy of the RNN model for the signal-to-noise ratio of ten satellites is higher than that of the Transformer model. The recurrent neural network model has an average accuracy of 64.76% in predicting the signal-to-noise ratio data, while the Transformer model has only 3%. In the RNN prediction model, the accuracy of the prediction for 7 out of 10 satellites signal-to-noise ratios is above 60%. It can be seen that the RNN model has a better prediction effect when facing the signal-to-noise ratio data of BeiDou satellite navigation signals with the time series data type. Therefore, the RNN model can realize the prediction of 0.08dB error for BeiDou signal-to-noise ratio, and when the difference between the future signal-to-noise ratio value and the predicted value is greater than 0.08 dB, it is considered that the signal is a spoofing signal at this time, so as to realize spoofing interference detection. The research results provide certain reference value for the research of satellite navigation spoofing algorithm.