Abstract
Global Navigation Satellite System (GNSS) data that is disturbed by jammer events is collected by the Advanced Radio Frequency Interference (RFI) Detection Analysis and Alerting System (ARFIDAAS). In this paper we present an automatic classification algorithm to categorize the observed jammer events into thirteen different jammer signal classes. The classification algorithm is based on functions and properties derived from the spectrogram of the data. The algorithm performance has first been validated using simulated/synthetic events. The information saved from the classification algorithm can be used to derive long term statistics on the occurrence of jammer signal types.
