Network-Based GNSS Jamming Prediction Enabling Wideband Interference Observation

Abstract

In this paper, we develop and evaluate autonomous, self-calibrating, receiver-independent C/N0-based jamming detection algorithms 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 8 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 design a portable experimental RF data collection setup and develop 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, USA, and validated the C/N0-based detector through time-frequency analysis of wideband RF data from the event.