Network-Based GNSS Jamming Prediction Enabling Wideband Interference Observation
Year: 2026
Type: article
Abstract: 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.
DOI: 10.33012/navi.763
Authors: Sandeep Jada, Mark Psiaki,, Mathieu Joerger
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W7154608965