In early September 2017, several space weather events triggered disturbed conditions of the near‐Earth space. The combination of two coronal mass ejection arrivals, associated with an X‐class flare, caused a strong geomagnetic storm on 7 and 8 September, thus inducing diffuse ionospheric phase scintillations on Global Navigation Satellite System (GNSS) signals. This work analyzes the effects and the actual impact of such phase scintillations on transionospheric Global Positioning System (GPS) signals and on related positioning accuracy. The research focuses in particular on high‐latitude GPS L1 data, recorded during a test campaign in Svalbard, Norway. The joint effect of satellites at low elevation and the exposure of ionosphere to the geospace forcing make navigation a critical task for such a challenging environment. Data analysis shows that the performance of carrier smoothing algorithms was affected by the presence of moderate and strong phase scintillation. It is shown in this study that positioning errors double when GPS signals affected by scintillation are used. This work shows that scintillations induce a considerable clustering effect on the smoothed positioning solutions; therefore, a methodology to automatically and autonomously detect the boundaries of the scintillation event is suggested according to such an high‐level effect. The use of software‐defined radio receivers for automatically capturing and processing GNSS data affected by scintillation is an added value to the analysis, as it offers the possibility to implement advanced signal processing techniques and a deeper observation of the impact of scintillations on the signals.

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.

Raw Global Navigation Satellite System (GNSS) measurements have been available since 2016 in select Android smartphones. The availability of such observations allows smartphones users, in principle, to significantly improve the quality of GNSS-based positioning by applying customized and advanced positioning algorithms. However, the quality of such measurements is poor, mainly because of the low quality of smartphone hardware components and the nonideal environment in which phones are typically used. To overcome this problem and to separate the contribution of the hardware components and signal quality, dedicated test campaigns were carried out in a real environment and in a controlled-environment anechoic chamber using several different Android models. In addition, signal-processing techniques aimed at increasing the accuracy and precision of the solution were employed. Results show that the quality of the data captured in the anechoic chamber was significantly better than in real conditions. Furthermore, such analysis allows to underline certain phenomena in smartphones, such as the duty cycle, and to test the validity of anechoic environments for Android raw measurements.

We aim at contributing to the reliability of the phase scintillation index on Global Navigation Satellite System (GNSS) signals at high-latitude. To the scope, we leverage on a recently introduced detrending scheme based on the signal decomposition provided by the fast iterative filtering (FIF) technique. This detrending scheme has been demonstrated to enable a fine-tuning of the cutoff frequency for phase detrending used in the phase scintillation index definition. In a single case study based on Galileo data taken by a GNSS ionospheric scintillation monitor receiver (ISMR) in Concordia Station (Antarctica), we investigate how to step ahead of the cutoff frequency optimization. We show how the FIF-based detrending allows deriving adaptive cutoff frequencies, whose value changes minute-by-minute. They are found to range between 0.4 and 1.2 Hz. This allows better accounting for diffractive effects in phase scintillation index calculation and provides a GNSS-based estimation of the relative velocity between satellite and ionospheric irregularities.