Machine Learning Image Recognition for GNSS Jamming Signals Categorization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00382199" target="_blank" >RIV/68407700:21260/24:00382199 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/NNW.2024.34.019" target="_blank" >https://doi.org/10.14311/NNW.2024.34.019</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2024.34.019" target="_blank" >10.14311/NNW.2024.34.019</a>

Result language
angličtina
Original language name
Machine Learning Image Recognition for GNSS Jamming Signals Categorization
Original language description
Global Navigation Satellite Systems are a critical positioning, navigation, and timing source for various industries. However, their weak signal on Earth’s surface makes them vulnerable to jamming. This paper explores the use of machine learning image recognition for categorizing GNSS jamming signals. The study uses data from a long-term monitoring campaign, with over 2,000 jamming events recorded. Seven commonly used jamming signal types were analyzed using the Residual Neural Networks (ResNet). Five different ResNet models with 18 to 152 layers were evaluated, with the best performing achieving a precision greater than 90% in determining the correct jamming signal category.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20304 - Aerospace engineering

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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