Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax

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Title: Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
Author: Franzese, Giulio; Linty, Nicola; Dovis, Fabio
Publisher: MDPI
Date: 2020
Language: en
Belongs to series: Applied Sciences
ISSN: 2076-3417
Abstract: This work focuses on a machine learning based detection of iono-spheric scintillation events affecting Global Navigation Satellite System (GNSS) signals. We here extend the recent detection results based on Decision Trees, designing a semi-supervised detection system based on the DeepInfomax approach recently proposed. The paper shows that it is possible to achieve good classification accuracy while reducing the amount of time that human experts must spend manually labelling the datasets for the training of supervised algorithms. The proposed method is scalable and reduces the required percentage of annotated samples to achieve a given performance, making it a viable candidate for a realistic deployment of scintillation detection in software defined GNSS receivers.
Subject: scintillation detection
convolutional neural network
semi-supervised learning

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