Machine Learning Image Recognition for GNSS Jamming Signals Categorization

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Image Recognition for GNSS Jamming Signals Categorization

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Image Recognition for GNSS Jamming Signals Categorization

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20304 - Aerospace engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Svazek periodika

34

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

20

Strana od-do

341-360

Kód UT WoS článku

001437878400002

EID výsledku v databázi Scopus

2-s2.0-86000143825