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Jammer Classification in GNSS Bands Via Machine Learning Algorithms

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Jammer Classification in GNSS Bands Via Machine Learning Algorithms. / Morales Ferre, Ruben; de la Fuente, Alberto; Lohan, Elena Simona.

julkaisussa: Sensors (Basel, Switzerland), Vuosikerta 19, Nro 22, 06.11.2019.

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Morales Ferre, Ruben ; de la Fuente, Alberto ; Lohan, Elena Simona. / Jammer Classification in GNSS Bands Via Machine Learning Algorithms. Julkaisussa: Sensors (Basel, Switzerland). 2019 ; Vuosikerta 19, Nro 22.

Bibtex - Lataa

@article{82c3bb8d4f8b4ab0a8442d130b3640de,
title = "Jammer Classification in GNSS Bands Via Machine Learning Algorithms",
abstract = "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 vector machines show up to 94 . 90 {\%} accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 {\%} accuracy in classification. The training and test databases generated for these tests are also provided in open access.",
author = "{Morales Ferre}, Ruben and {de la Fuente}, Alberto and Lohan, {Elena Simona}",
year = "2019",
month = "11",
day = "6",
doi = "10.3390/s19224841",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI",
number = "22",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Jammer Classification in GNSS Bands Via Machine Learning Algorithms

AU - Morales Ferre, Ruben

AU - de la Fuente, Alberto

AU - Lohan, Elena Simona

PY - 2019/11/6

Y1 - 2019/11/6

N2 - 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 vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

AB - 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 vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

U2 - 10.3390/s19224841

DO - 10.3390/s19224841

M3 - Article

VL - 19

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 22

ER -