Abstract
Global Navigation Satellite System (GNSS) signals are increasingly affected by man-made intentional interferences, such as jamming and spoofing, underscoring the need for reliable and extensively evaluated interference detection techniques in protected GNSS frequency bands. This paper presents a GNSS signal anomaly detection method based on a Chi-Square statistical test applied directly to raw digitized intermediate-frequency (IF) samples, where an anomaly is defined as any man-made interference signal. A key contribution of this work lies in its extensive experimental evaluation, which spans a wide range of datasets, including multiple publicly available spoofing datasets that have been widely used by the research community, as well as data collected during a real-world GNSS jammer test campaign held in Norway in 2023 (JammerTest2023). The proposed method is implemented using the open-source software-defined receiver FGI-GSRx and evaluated under diverse and realistic signal propagation conditions. The results demonstrate anomaly detection rates exceeding 99% across all evaluated datasets, with no false alarms observed within the tested scenarios. The GNSS datasets used in this study, collected during the JammerTest2023 campaign, are made publicly available. The open-source FGI-GSRx software-defined receiver along with the configuration files required to reproduce the reported results, is also provided. This open release enables independent reproduction of the results and supports systematic comparison with state-of-the-art GNSS interference detection techniques.