Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Linty, Nicola; Farasin, Alessandro; Favenza, Alfredo; Dovis, Fabio

doi:10.1109/taes.2018.2850385

Cited by 79 publications

(55 citation statements)

References 34 publications

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“…Plasmaspheric electron density estimation has been proposed in Zhelavskaya et al, (, ). Concerning the ionosphere‐thermosphere region, ionospheric scintillation has been modeled in Jiao et al (), Lima et al (), Linty et al (), McGranaghan et al (), and Rezende et al (). The estimation of maps of total electron content (TEC) has been tackled in Acharya et al (), Habarulema et al (), Habarulema et al (), Hernandez‐Pajares et al (), Leandro and Santos (), Watthanasangmechai et al (), Wintoft and Cander (), and Tulunay et al ().…”

Section: Review Of Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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Section: Review Of Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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“…The aim of this paper is then to develop a new scintillation detection strategy based on Convolutional Neural Networks and semi-supervised learning. In more detail, we extend the results and approaches presented in [11], in which classification of scintillation events is performed using supervised decision trees. While showing good performance, this method has two fundamental problems.…”

Section: Introductionmentioning

confidence: 94%

“…An updated version of the algorithm is presented in [10]. Other works propose using decision tree and random forest algorithms and low level signal observables, such as the in-phase and quadrature correlation outputs of the receiver tracking loop [11,12]. This approach is able to reduce the false alarms rate due to the ambiguity between scintillation and multipath typical of the classical approaches based on the analysis of amplitude scintillation index and provide an early run-time alert.…”

Section: Introductionmentioning

confidence: 99%

“…The biggest problem with machine learning based classification is that datasets must be labeled manually by experts, wasting precious resources in terms of time, and thus preventing large scale scalability of the method [8,11]. The techniques proposed in the literature and described above partially solve the problem as they are based on fully-supervised learning: a significant portion of input data must be manually labeled to assist the learning process of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax

2020

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This work focuses on a machine learning based detection of ionospheric 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.

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“…Supervised learning is a widely known machine learning (ML) techniques whereby a training set with known labels/output is used to train a model for the purpose of predicting future outputs [6]. To generalise new instances, either a classifier or a regression is created from the rule of set [7]. Within this subset of ML, some of the mapping forms of the prediction function used include Decision tree (DT), Logistic regression (LR), Neural networks (NN) and support vector machines (SVM).…”

Section: Introductionmentioning

confidence: 99%