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.
In urban contexts, the increasing density of electronic devices equipped with Global Navigation Satellite System (GNSS) receivers and complementary positioning technologies is attracting research and development efforts devoted to an improvement of the quality of life towards the smart city paradigm. Vehicular and pedestrian positioning and navigation capabilities are among the major drivers for innovation in this process. Ultra-low-cost electronics such as smartphones and Internet of Things (IoT) sensors aim at providing accurate and reliable positioning solutions through a set of promising solutions. Among these, snapshot positioning allows to remotely perform the post-processing of GNSS signals in IoT sensor networks while Wi-Fi™ ranging and cooperative positioning provide auxiliary anchors of opportunity to enhance indoor/outdoor positioning capabilities. This paper presents an innovative platform to perform a centralised testing and assessment of such positioning and navigation technologies along with a set of results obtained in the context of the European project HANSEL, by relying on current network technologies and infrastructures (i.e., Wi-Fi™ and cellular connectivity).
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.
Previous research contributions have addressed the definition of a Cramer Rao Lower Bound (CRLB) to investigate the performance of hybrid positioning algorithms that exploit satellite-based range measurements and independent terrestrial range measurements. Starting from such results, this work investigates the quantity of information carried by terrestrial relative measurements obtained from the combination of satellite-based range measurements shared among pairs of connected agents.The study is conceived to investigate the impact of such relative ranges on the positioning error when they are used as additional measurements to help improving accuracy and precision of positioning. By considering some prior knowledge about satellite-to-user and userto-user ranging uncertainties, the approximation of a theoretical limit for this novel class of hybrid positioning algorithms allows to observe when the use of cooperative ranges is beneficial, depending on their variance and on the geometry of satellites and terrestrial agents.
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