Comparative Performance Study of Linear and Gaussian Kernel SVM Implementations for Phase Scintillation Detection

The aim of this paper is to analyze the design of support vector machine (SVM) algorithm that belongs to the class of supervised machine learning algorithms for phase scintillation detection and to discuss the performance comparison of linear and Gaussian kernel implementations by considering the de...

Full description

Bibliographic Details
Published in:2019 International Conference on Localization and GNSS (ICL-GNSS)
Main Authors: Savas, Caner, Dovis, Fabio
Format: Conference Object
Language:English
Published: IEEE 2019
Subjects:
GNSS
Machine Learning
Antarctic
SANAE
SANAE IV
Base SANAE IV
Antarc*
Online Access:http://hdl.handle.net/11583/2746932
https://doi.org/10.1109/ICL-GNSS.2019.8752635
https://ieeexplore.ieee.org/document/8752635
Description
Summary:The aim of this paper is to analyze the design of support vector machine (SVM) algorithm that belongs to the class of supervised machine learning algorithms for phase scintillation detection and to discuss the performance comparison of linear and Gaussian kernel implementations by considering the design parameter’s effects. The algorithm processes the phase scintillation indices computed for GPS L1 signals through the designed linear and Gaussian kernel SVM models. The study is based on the real GNSS signals which are affected by phase scintillations, collected at South African Antarctic research base (SANAE IV).

Similar Items