CSL-based Detection Method for BeiDou Spoofing Interference Signals

Anti-jamming and anti-spoofing technologies for the BeiDou Navigation Satellite System (BDS) have become major challenges in current research. Due to the openness of civilian signals and power limitations, user terminals are highly vulnerable to various attacks, including spoofing interference. This paper proposed a CNN-SA-LSTM (CSL) model for BDS spoofing interference detection method based on a CNN-LSTM combined model, leveraging discriminative features derived from signal reception characteristics. The model utilizes a combination of signal characteristics, including pseudorange, carrier phase, carrier-to-noise ratio, and Doppler shift, to detect BDS signal spoofing interference. A field test was conducted, and the simulation results showed that compared with single CNN, RNN, or LSTM models, the combined detection model proposed in this paper can effectively detect multiple types of interference data, with a detection accuracy rate of 93.56%, which is higher than that of single models. This shows that the method has strong pattern recognition and generalization capabilities, significantly improving detection accuracy and robustness.