Survey on Signal Processing for GNSS under Ionospheric Scintillation: Detection, Monitoring, and Mitigation

Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high-precision GNSS receivers and safety critical applications where accuracy, availability, continuity and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal processing techniques such as adaptive/robust methods, machine learning or parameter estimation. This contribution reviews the signal processing landscape in GNSS receivers, with emphasis on different detection, monitoring and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed

Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree

This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts

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 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

Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms

L'abstract è presente nell'allegato / the abstract is in the attachmen

Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax

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

Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm

This paper presents a machine learning model able to distinguish between ionospheric scintillation and multipath in GNSS-based scintillation monitoring data. The inputs to the model are the average signal intensity, the variance in the signal intensity, and the covariance between the in-phase and the quadrature-phase outputs of the tracking loop of a GNSS receiver. The model labels the data as either scintillated, multipath affected, or clean GNSS signal. The overall accuracy of the model is 96% with 2% miss-detection rate and a negligible false alarm rate for the scintillation class in particular. The gain in the amount of scintillation data is up to 17.5% that would have been discarded if an elevation mask of 30° was implemented

Detecting Phase Scintillation at High Latitudes Using Ionospheric Scintillation Monitoring Records and Machine Learning Techniques

In this paper, we present a bagged tree model able to detect phase scintillation at high latitudes with 95% accuracy, 5% scintillation miss-detection and 5% scintillation false alarm. The input to the model is a series of 3 minutes of the Total Electron Content (TEC), 3 minutes of the change in TEC (dTEC), and the satellite elevation. These values are extracted from Ionospheric Scintillation Monitoring Records (ISMR) logged by Ionospheric Scintillation Monitoring (ISM) receivers. We compare the performance of this model to Support Vector Machine (SVM) models, k-Nearest Neighbors (k-NN) models, and also to other decision tree models. Furthermore, we assess the ability of the TEC and dTEC features to detect scintillation independently of the scintillation indexes. For this, we compare the above decision trees, kNN and SVM models to the same models but trained using scintillation indexes as additional inputs. Moreover, we show the results of testing the proposed model using a novel data set. Finally, we compare the accuracy of the machine learning model to the performance of a detector based on the phase scintillation index σ ϕ threshold

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) 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 ML 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.Comment: under revie

Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day-to-day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time-series into a 6-dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset