A Satellite Navigation Spoofing Interference Detection Method Based on LSTM

Facing the problem of satellite navigation spoofing interference detection, this paper proposes a feature parameter selection and data processing method applicable to the detection of spoofing interference by Long Short-term Memory (LSTM) neural network. Based on the analysis of the effect of spoofing interference on the receiver loop, the direct and indirect information in the tracking loop is selected as the spoofing detection feature parameters; the feature parameters are preprocessed using the sliding window technique, and the training and test sets are constructed according to the time series to ensure the temporal relevance of the features. Based on the experiments conducted on the TEXBAT dataset from the University of Texas at Austin, this paper's method can improve the accuracy of spoofing detection by 2.19% and spoofing scene recognition by 99.98% compared to the existing SVM methods, which can effectively solve the problem of navigation spoofing detection and scene recognition.