A Scalable Pipeline for Real-Time Global Detection and Localization of GNSS Interference Using ADS-B

Zixi Liu, Sherman Lo, Yu-Hsuan Chen, Todd Walter

Abstract:	GNSS jamming and spoofing have emerged as critical threats to aviation safety and navigation reliability. Current detection methods rely heavily on pilot/operator reports or regional ground sensors, which are not scalable or real-time. We present an automated, near real-time, globally scalable pipeline leveraging Automatic Dependent Surveillance–Broadcast (ADS-B) data. The system integrates Bayesian Online Changepoint Detection (BOCPD) for jamming detection, Kalman filter–based velocity monitoring for spoofing, DBSCAN clustering for event determination, and line-of-sight (LOS) analysis for localization. From 2022–2025, the system has continuously monitored global air traffic, detecting and localizing interference with localization accuracy within 0.01 deg and detection delays under 5 minutes. A public website (rfi.stanford.edu) and Python package make all detection results available for analysis, enabling transparency and broad access.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	446 - 456
Cite this article:	Liu, Zixi, Lo, Sherman, Chen, Yu-Hsuan, Walter, Todd, "A Scalable Pipeline for Real-Time Global Detection and Localization of GNSS Interference Using ADS-B," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 446-456. https://doi.org/10.33012/2025.20358
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