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Research on BeiDou B1C Signal Abnormal Monitoring Algorithm Based on Machine Learning

High-precision systems such as civil aviation have put forward higher requirements for navigation systems, including indicators such as accuracy and integrity. Signal distortions and evil waveforms (EWF) generated by the signal-generating hardware on the satellite can severely affect the cross-correlation function of the signal, thereby affecting the integrity of the navigation system. With the further development of the BeiDou Navigation System (BDS), the types of signal distortion are subdivided into three types: analog distortion, subcarrier distortion, and PN code distortion. Traditional multi-correlator methods are no longer applicable under the requirements of modern navigation systems. In this paper, a machine learning-based BeiDou B1C signal anomaly monitoring algorithm is proposed. We detected and classified the signals using a quadratic discriminant analysis (QDA) method. The results show that our method can accurately classify the distortion types under the condition that the accuracy of distortion detection can be greatly improved. Meanwhile, our method is also highly effective and robust.