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Article

The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Departments of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
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This paper is an extended version of “Comparative Performance Study of Linear and Gaussian Kernel SVM Implementations for Phase Scintillation Detection” published in the Proceedings of the 2019 9th International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany, 4–6 June 2019.
Sensors 2019, 19(23), 5219; https://doi.org/10.3390/s19235219
Received: 21 October 2019 / Revised: 22 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
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. View Full-Text
Keywords: GNSS; scintillation; support vector machines; kernel; Gaussian; polynomial GNSS; scintillation; support vector machines; kernel; Gaussian; polynomial
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MDPI and ACS Style

Savas, C.; Dovis, F. The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines. Sensors 2019, 19, 5219. https://doi.org/10.3390/s19235219

AMA Style

Savas C, Dovis F. The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines. Sensors. 2019; 19(23):5219. https://doi.org/10.3390/s19235219

Chicago/Turabian Style

Savas, Caner, and Fabio Dovis. 2019. "The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines" Sensors 19, no. 23: 5219. https://doi.org/10.3390/s19235219

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