The recent access to GNSS (Global Navigation Satellite System) phase observations on smart devices, enabled by Google through its Android operating system, opens the possibility to apply precise positioning techniques using off-the-shelf, mass-market devices. The target of this work is to evaluate whether this is feasible, and which positioning accuracy can be achieved by relative positioning of the smart device with respect to a base station. Positioning of a Google/HTC Nexus 9 tablet was performed by means of batch least-squares adjustment of L1 phase double-differenced observations, using the open source goGPS software, over baselines ranging from approximately 10 m to 8 km, with respect to both physical (geodetic or low-cost) and virtual base stations. The same positioning procedure was applied also to a co-located u-blox low-cost receiver, to compare the performance between the receiver and antenna embedded in the Nexus 9 and a standard low-cost single-frequency receiver with external patch antenna. The results demonstrate that with a smart device providing raw GNSS phase observations, like the Nexus 9, it is possible to reach decimeter-level accuracy through rapid-static surveys, without phase ambiguity resolution. It is expected that sub-centimeter accuracy could be achieved, as demonstrated for the u-blox case, if integer phase ambiguities were correctly resolved.

goGPS is a positioning software application designed to process single-frequency code and phase observations for absolute or relative positioning. Published under a free and open-source license, goGPS can process data collected by any receiver, but focuses on the treatment of observations by low-cost receivers. goGPS algorithms can produce epoch-by-epoch solutions by least squares adjustment, or multi-epoch solutions by Kalman filtering, which can be applied to either positions or observations. It is possible to aid the positioning by introducing additional constraints, either on the 3D trajectory such as a railway, or on a surface, e.g., a digital terrain model. goGPS is being developed by a collaboration of different research groups, and it can be downloaded from http://​www.​gogps-project.​org. The version used in this manuscript can be also downloaded from the GPS Toolbox Web site http://​www.​ngs.​noaa.​gov/​gps-toolbox. This software is continues to evolve, improving its functionalities according to the updates introduced by the collaborators. We describe the main modules of goGPS along with some examples to show the user how the software works

goGPS is a free and open source satellite positioning software package aiming to provide a collaborative platform for research and teaching purposes. It was first published in 2009 and since then several related projects are on-going. Its objective is the investigation of strategies for enhancing the accuracy of low-cost single-frequency GPS receivers, mainly by relative positioning with respect to a base station and by a tailored extended Kalman filter working directly on code and phase observations. In this paper, the positioning algorithms implemented in goGPS are presented, emphasizing the modularity of the software design; two specific strategies to support the navigation with low-cost receivers are also proposed and discussed, namely an empirical observation weighting function calibrated on the receiver signal-to-noise ratio and the inclusion of height information from a digital terrain model as an additional observation in the Kalman filter. The former is crucial when working with high-sensitivity receivers, while the latter can significantly improve the positioning in the vertical direction. The overall goGPS positioning accuracy is assessed by comparison with a dual-frequency receiver and with the positioning computed by a standard low-cost receiver. The benefits of the calibrated weighting function and the digital terrain model are investigated by an experiment in a dense urban environment. It comes out that the use of goGPS and low-cost receivers leads to results comparable with those obtained by higher level receivers; goGPS has good performances also in a dense urban environment, where its additional features play an important role.

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The Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.