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Rapid and sudden fluctuations of phase and amplitude in Global Navigation Satellite System (GNSS) signals due to diffraction of the ionosphere phase components when signals passing through small-scale irregularities (less than hundreds meters) are commonly so-called ionospheric scintillation. The aim of the paper is to analyze the implementation and compare the performance of different phase detrending algorithms to improve scintillation monitoring. Three different phase detrending methods, namely, three cascaded second-order high pass filters, six order Butterworth filter conducted by cascading six first-order high pass Butterworth filters, and Fast Iterative Filter (FIF) are considered in this paper. The study exploits real GNSS signals (GPS L1, Galileo E1b) affected by significant phase scintillation effects, collected in early September 2017 at Brazilian Centro de Radioastronomia e Astrofisica Mackenzie (CRAAM) monitoring station and at Adventdalen (Svalbard, Norway) research station. In this study, a softwaredefined radio (SDR) based GNSS receiver is used to process GNSS signals and to implement the aforementioned detrending algorithms.

GNSS is widely used to provide positions in an absolute reference frame in Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), where GNSS is merged with the information provided by other sensors. Even if the main goal of GNSS signal processing is the positioning, multifrequency signals are a rich source of information about the propagation environment surrounding the mobile vehicle. In urban and harsh environment, situational awareness is essential to tailor the operations and take proper countermeasure to harsh propagation conditions. Given this framework the present paper will describe the use of GNSS as signals of opportunity for the characterization of the operative environment by processing the GNSS observables through Machine Learning (ML) algorithms that can be used as efficient features extractors. The paper will present some case studies of operational scenarios for UGVs and for a static monitoring station, showing how through combining DSP techniques with both unsupervised and supervised ML algorithms (K-means classes, Support Vector Machines) it is possible to retrieve the information about the propagation scenario for multipath, interference and atmospheric limitations.

The aim of this paper is to propose a multipath detection algorithm, which is based on K-means clustering that belongs to the class of unsupervised machine learning algorithms. The algorithm processes measurement sets computed for each satellite, namely, carrier phase, pseudorange and carrier-to-noise ratio, creating clusters of consistent measurements, thus allowing the identification of satellite signals suffering from the multipath error. Since it is an unsupervised method, it overcomes one of the most limiting features of supervised algorithms that require training data sets a-priori obtained as representative of multipath and no-multipath conditions. The study exploits both the real GNSS data affected by the multipath in the surrounding environment that were collected at South African Antarctic research base SANAE-IV and the simulated data where the ionospheric, tropospheric and multipath errors are modelled. Receiver Autonomous Integrity Monitoring (RAIM) algorithm with parity method was also implemented and tested for the same datasets, and it will be used as a term of comparison for the algorithm performance.