On the Edge Model-Aided Machine Learning GNSS Interference Classification with Low-Cost COTS Hardware

Interference signals can disrupt global navigation satellite system (GNSS) receivers and degrade or deny a position-velocity-time (PVT) solution. After detecting an interference signal, classifying its type can provide insight into its cause and help determine the necessary next steps to counteract it. In this paper, we present a method for interference detection and a resource-efficient model-aided on-the-edge machine learning (ML) model for interference signal classification running on low-cost commercial-off-the-shelf (COTS) hardware, enabling a highly cost-effective spectral monitoring solution. The choice of detection metrics is justified based on real-world spectral monitoring data from a German highway and the capability of the ML model to generalize across different environments is demonstrated in an outdoor field test. Overall, we present an operationally ready GNSS interference detection and classification system.