|Abstract:||The growing dependence of critical infrastructure on Global Navigation Satellite Systems (GNSS) as an accurate and reliable positioning, navigation and timing (PNT) source gives rise to the importance of GNSS interference detection. Although jamming detection capabilities are present in the current market, predominately in the form of specialised GNSS interference detectors or GNSS receivers add-ons. These provide a limited coverage area and their implementation into critical infrastructure operations is rather slow. Therefore, this paper focuses on the detection of GNSS interference using widespread Automatic Dependent Surveillance-Broadcast (ADS-B) technology. The research builds upon previous work and addresses some of its limitations by developing a discrete mathematical model for GNSS jamming detection based on ADS-B quality parameters. To develop and validate the model, a series of experiments involving GNSS jamming in live-sky environments were conducted. The controlled experiments enabled close monitoring of the aircraft navigation systems allowing for precise determination of the aircraft’s jammed/unjammed status. Approximately 75% of the jamming experiment data was used for model development and tuning, while the remaining 25% was reserved for evaluation. The model evaluation leveraging the confusion matrix showed a positive jamming detection rate of over 99% and a false positive jamming detection rate of under 1%. Additionally, the model was tested on ADS-B data from the Atlantic Ocean where no GNSS jamming is expected. Using this data set the model exhibited an under 1% false positive jamming detection rate.|
Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)
September 11 - 15, 2023
Hyatt Regency Denver
|Pages:||4145 - 4152|
|Cite this article:||
Steiner, Jakub, Nagy, Ivan, "Discrete Mathematical Model for GNSS Interference Detection Using ADS-B Quality Parameters," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 4145-4152.
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