Global Navigation Satellite Systems as Signals of Opportunity for Environmental Applications: Reflectometry and Scintillation

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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

Data from GNSS-Based Passive Radar to Support Flood Monitoring Operations

Signals transmitted by Global Navigation Satellite Systems can be exploited as signals of opportunity for remote sensing applications. Satellites can be seen as spread sources of electromagnetic radiation, whose signals reflected back from ground can be processed to detect and monitor geophysical properties of the Earth’s surface. In the past years, several experiments of GNSS-based passive radars have been demonstrated successfully, mainly from piloted aircraft. Then, the proliferation of small UAVs enabled new applications where GNSS-based passive radars can provide useful geospatial information for environmental monitoring. Thanks to the availability of commercial Radio Frequency front ends and the enhanced processing capabilities of embedded platforms, it is possible to develop GNSS-based passive radars at moderated cost. These can be mounted on Unmanned Aerial Vehicles, and be used to support the sensing of environmental parameters. This paper presents the results of an experimental campaign based on the use of a UAV for GNSS reflectometry, tailored to the detection of the presence of water on ground after floods. The work is part of wider project, which intends to develop solutions to support rescuers and decision makers to manage operations after natural disasters, through the integration and modelling of geospatial data coming from multiple sources