Implementation and performance analysis of a Chi-Square Test based GNSS signal anomaly detection

Abstract Global Navigation Satellite System (GNSS) signal anomalies in the form of man-made intentional interferences (i.e., jamming or spoofing) have raised the need for effective detection, localization, classification, and mitigation of such unwanted interferences on those protected frequency bands. This work introduces a novel GNSS signal anomaly detection technique that employs a Chi-Square Test on raw digitized intermediate frequency (IF) samples to identify any interference signals on GNSS frequency bands. In this context, ’anomaly’ refers to any man-made interference, including jamming or spoofing. The proposed technique is implemented, tested, and its performance analyzed using an open-source software-defined receiver named FGI-GSRx across various publicly available GNSS data sources and a real-world jammer test campaign. The results demonstrate that the Chi-Square Test achieves an impressive anomaly detection accuracy of over 99% with no false alarms across all datasets representing realistic signal propagation environments. Additionally, the raw GNSS data samples from the jammer test campaign are publicly shared to enable other researchers to develop, test, and validate anomaly detection and mitigation techniques using common datasets.