GNSS Interference Detection Using Machine Learning Algorithms on ADS-B Data

Zixi Liu, Sherman Lo, Todd Walter

Abstract: Global Navigation Satellite System (GNSS) interference events that occur near airports can cause severe safety issues by denying GNSS based approaches and landings. Current solutions for interference detection and localization (IDL) such as using radio direction finding are generally costly and time-consuming. The approach described in this paper uses Automatic Dependent Surveillance—Broadcast (ADS-B) reports for IDL and applies machine learning algorithms to this data. We utilized a standard neural network (NN) and a convolutional neural network (CNN) to detect GNSS interference event in a given airspace. These models take airplane's ADS-B reports as inputs and output a classification of whether this airplane has experienced jamming. With this approach, we achieved 83.6% and 90.4% accuracy for corresponding model. We used logistic regression as a baseline model which achieved an 75.1% accuracy. This paper has two main objectives. First, our NN model is used to determine the size and shape of the jammer impact region. By achieving this purpose, we are able to identify the complicated environmental factors from local airspace such as the signal blockage caused by the mountains, which are commonly difficult to identify or represent using mathematical models. Second, our CNN model is used for identifying the most likely location of the interference source which only requires ADS-B data collected within few hours’ time window from target airspace. The key significant step to this approach is that it is mimicking the way of how human identifies the location of interference source which is by looking at the overall picture of the airspace. In addition, it also learns the complicated reasons of how interference source could cause impact on the overall picture of ADS-B data in current airspace. In order to feed ADS-B data into CNN, we designed a method to convert 1-D structured ADS-B data into higher-dimensional matrix. This data preprocessing allows us to apply convolutional neural networks to this topic.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 4305 - 4315
Cite this article: Liu, Zixi, Lo, Sherman, Walter, Todd, "GNSS Interference Detection Using Machine Learning Algorithms on ADS-B Data," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 4305-4315.
https://doi.org/10.33012/2021.18111
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