Research on GNSS spoofing detection method based on tracking loop moving average processing

A spoofing detection algorithm based on Tracking Loop Correlation Output (TLCO) is proposed to address the issues of limited applicability and significant fluctuations in detection performance of traditional detection algorithms. To suppress noise, the TLCO Moving Average (TLCO-MA) detection algorithm is further proposed. The construction of TCCO-MA uses the output of correlators to multiply and linearly weight the TLCO detection metric, and uses skewness and kurtosis to jointly verify its approximate normality. Then, the TLCO is subjected to MA filtering to obtain the TLCO-MA detection metric. Conduct spoofing detection experiments using DS2, DS3, and DS5 from the TEXBAT database of the University of Texas, and analyze the detection performance of the proposed algorithm and traditional SQM algorithms in multiple scenarios, including detection probability, sensitivity, immediacy, detection range, and robustness. The experimental results show that compared with the traditional SQM algorithm, TLCO-MA has better detection performance and a wider range of applicable scenarios.