The authentication of GNSS signals poses a challenging problem. Before exploiting a signal (e.g. its pseudo-noise sequence) it would be desirable for the receiver to have assurance quickly (ideally in less than 10-s) and reliably that the signal is actually coming from a navigation satellite and not from a spoofer (e.g. unknown pseudolite, delayed version of the same signal). Classical data-level authentication solutions work after signal demodulation (PN sequence de-spreading), require data-level cryptography (shared secrets) and key management.

High-sensitivity GNSS receivers architectures are of interest in harsh propagation conditions in order to be able to keep tracking GNSS signals even at very low C/No conditions (typically down to around 10-20 dBHz, depending on the signal being tracked). For this purpose, open-loop techniques can be used in the estimation of the code delay and the carrier phase and/or frequency, targeting a more robust estimation of those observables. In this process, advanced signal estimation techniques can be applied for the optimum exploitation of e.g. multi-correlation samples.

The ENHANCE (Enabling Harbor to Harbor Autonomous Situational Awareness in Sea Ice Conditions) project is aimed to study How to improve the autonomous navigation and situational awareness in the maritime domain by utilising ML and AI concepts. For this end, we utilise both traditional RGB cameras as well as IR cameras to obtain as diverse as possible data set of sea-ice conditions to train a ML model that can identify an ice tracks and objects, such as buoys, that can help in navigation.

Mass market receivers are currently basing the provision of position and navigation functionalities by using a number of different signals (GNSS as primary means of navigation, cellular network etc.). The introduction of Galileo and Assisted Galileo services in the mass market applications, will bring significant benefits to the users, especially for applications in harsh conditions and environments, like urban/suburban.

The possibility to acquire and track signals for which the PRN code is not public (e.g. as for Galileo Public Regulated Service or any other non-public GNSS service) is of high interest in order to be able to assess, with real field signal, new acquisition and tracking techniques that otherwise can only be assessed with a full GNSS receiver with access to those PRN codes. In this context, the recovery of the unknown PRN codes from low-noise signal recordings (e.g.

This project is conceived at analysing a secured communication link between a space-based system and a maritime system. The final goal is to serve autonomous navigation vessels, with a possible extension to other smart commercial maritime services and to favour the process towards a highly and full digitalisation of the Port system. The autonomous ship is here considered from an operating point of view, although quite simple and with the purpose of understanding the impact on the telecommunication system. The initial focus is on three possible telecommunication solutions:

Currently GNSS Systems provide services for instantaneous absolute positioning accuracy of a few meters, assuming no local obstructions above 5-10 degrees elevation, based on the navigation signals broadcast by a MEO constellation.

The objective of the proposed PNT Traffic Light project is to develop a continuous 24/7 performance monitoring tool for EGNOS/GNSS (and particularly for GPS, GALILEO and EGNOS) on the northern and arctic region of, at least, EGNOS coverage area. In addition to just EGNOS, the tool shall be designed to support assessment of the performance of also other GNSSs (at least GPS and Galileo).