The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines
Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acqui...
Published in: | Sensors |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/11583/2770561 https://doi.org/10.3390/s19235219 https://www.mdpi.com/1424-8220/19/23/5219 |
id |
ftpoltorinoiris:oai:iris.polito.it:11583/2770561 |
---|---|
record_format |
openpolar |
spelling |
ftpoltorinoiris:oai:iris.polito.it:11583/2770561 2024-02-11T09:56:58+01:00 The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines Savas, Caner Dovis, Fabio Savas, Caner Dovis, Fabio 2019 ELETTRONICO http://hdl.handle.net/11583/2770561 https://doi.org/10.3390/s19235219 https://www.mdpi.com/1424-8220/19/23/5219 eng eng MDPI info:eu-repo/semantics/altIdentifier/wos/WOS:000507606200162 volume:19 issue:23 firstpage:1 lastpage:16 numberofpages:16 journal:SENSORS info:eu-repo/grantAgreement/EC/H2020/corda__h2020::6e9a7dd20047847f41d755f7cb46bbeb http://hdl.handle.net/11583/2770561 doi:10.3390/s19235219 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075745312 https://www.mdpi.com/1424-8220/19/23/5219 info:eu-repo/semantics/openAccess GNSS scintillation support vector machine kernel Gaussian polynomial info:eu-repo/semantics/article 2019 ftpoltorinoiris https://doi.org/10.3390/s19235219 2024-01-16T23:15:01Z Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time. Article in Journal/Newspaper Antarc* Antarctic PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino) Antarctic Base SANAE IV ENVELOPE(-2.850,-2.850,-71.667,-71.667) SANAE ENVELOPE(-2.850,-2.850,-71.667,-71.667) SANAE IV ENVELOPE(-2.850,-2.850,-71.667,-71.667) Sensors 19 23 5219 |
institution |
Open Polar |
collection |
PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino) |
op_collection_id |
ftpoltorinoiris |
language |
English |
topic |
GNSS scintillation support vector machine kernel Gaussian polynomial |
spellingShingle |
GNSS scintillation support vector machine kernel Gaussian polynomial Savas, Caner Dovis, Fabio The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
topic_facet |
GNSS scintillation support vector machine kernel Gaussian polynomial |
description |
Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time. |
author2 |
Savas, Caner Dovis, Fabio |
format |
Article in Journal/Newspaper |
author |
Savas, Caner Dovis, Fabio |
author_facet |
Savas, Caner Dovis, Fabio |
author_sort |
Savas, Caner |
title |
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
title_short |
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
title_full |
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
title_fullStr |
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
title_full_unstemmed |
The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines |
title_sort |
impact of different kernel functions on the performance of scintillation detection based on support vector machines |
publisher |
MDPI |
publishDate |
2019 |
url |
http://hdl.handle.net/11583/2770561 https://doi.org/10.3390/s19235219 https://www.mdpi.com/1424-8220/19/23/5219 |
long_lat |
ENVELOPE(-2.850,-2.850,-71.667,-71.667) ENVELOPE(-2.850,-2.850,-71.667,-71.667) ENVELOPE(-2.850,-2.850,-71.667,-71.667) |
geographic |
Antarctic Base SANAE IV SANAE SANAE IV |
geographic_facet |
Antarctic Base SANAE IV SANAE SANAE IV |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000507606200162 volume:19 issue:23 firstpage:1 lastpage:16 numberofpages:16 journal:SENSORS info:eu-repo/grantAgreement/EC/H2020/corda__h2020::6e9a7dd20047847f41d755f7cb46bbeb http://hdl.handle.net/11583/2770561 doi:10.3390/s19235219 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075745312 https://www.mdpi.com/1424-8220/19/23/5219 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/s19235219 |
container_title |
Sensors |
container_volume |
19 |
container_issue |
23 |
container_start_page |
5219 |
_version_ |
1790607439712747520 |