Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Detection and classification of these interference signals allow better situational awareness and facilitate appropriate countermeasures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This dataset is the result of a proposal for a low-resource interference detection and classification approach that combines conventional statistical signal processing approaches with machine learning (ML).


Three raw (i.e., In-Phase and Quadrature data with a software radio, and observation files) GNSS dataset were recorded using a LabSat Version 3 inside of the West Virginia University  greenhouse and two outside recordings were also made to provide a quality reference and comparison. The outdoor location had to be an ideal location for satellite signal reception  and  the  indoor  location  was  a  greenhouse  room  where satellite visibility was limited, susceptible to attenuation, occlusion and multipath.