Enhanced GNSS Threat Detection: On-Edge Statistical Approach with Crowdsourced Measurements and Fuzzy Logic Decision-Making

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while Machine Learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C/N0-based crowdsourcing metric. Experiments show over 75% jamming and 95% spoofing detection precision, with false positive rates around 1–2% and an average detection time of 10 seconds.