Complementary Fusion of Autocorrelation Function Distortion and Power Anomalies for Enhanced GNSS Spoofing Detection

The use of Signal Quality Monitoring (SQM) methods for the detection of global navigation satellite system (GNSS) spoofing has been extensively studied. However, existing SQM methods struggle to balance correlator resources and detection performance. To overcome this issue, an enhanced spoofing detection method based on joint detection metrics is proposed. The proposed method integrates two complementary metrics: a correlation-based metric to capture spoofing-induced distortions in autocorrelation function (ACF), and an energy-driven metric to detect anomalous power variation. These metrics are combined through a logical OR fusion strategy, leveraging their complementary responses to distinct stages of spoofing. The simulation results show that the proposed method can detect more than 96% of the spoofing signals with various code phase and carrier phase at a false alarm probability of 0.01%. Validation on the Texas Spoofing Test Battery (TEXBAT) datasets further confirms performance, achieving a 95% average detection rate under identical false alarm probability. The method can be implemented with only four additional pairs of correlators, achieving a better balance between correlator resources and detection performance compared to previous SQM methods.