Jammer Classification in GNSS Bands Via Machine Learning Algorithms.
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ABSTRACT:
SUBMITTER: Morales Ferre R
PROVIDER: S-EPMC6891345 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
Publications
Jammer Classification in GNSS Bands Via Machine Learning Algorithms.
Morales Ferre Ruben R de la Fuente Alberto A Lohan Elena Simona ES
Sensors (Basel, Switzerland) 20191106 22
This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support ...[more]
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