Network-Based GNSS Jamming Prediction Enabling Wideband Interference Observation

<title>Abstract</title> In this paper, we develop and evaluate an autonomous, self-calibrating, receiver-independent carrier-to-noise-density ratio (C/N0)-based jamming detection algorithm capable of processing data from large receiver networks. The algorithm uses optimal detectors that target a predefined false alert rate. Using this algorithm, we processed eight months of data from hundreds of receivers and identified patterns in jamming detection consistent with intentional interference, providing an opportunity to validate the C/N0 detector. We designed a portable experimental radio frequency (RF) data collection setup and developed an optimal power-based jamming monitor to independently detect jamming. With this setup, we detected a genuine jamming event while driving on I-25 in Colorado, United States, and validated the C/N0-based detector through time–frequency analysis of wideband RF data from the event.