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119 - Dual Polarization Arrays for compact resilient GNSS Receivers
The ability of exploiting the spatial dimensions has made array antennas valuable for many applications, e.g., multipath and interference mitigation, spatial diversity, and localization. Consequently, over the past years, array antennas, i.e. Controlled Receptions Pattern Antennas (CRPA), have been deployed with GNSS receivers either to provide a spatial filter (null steering techniques) or to…
118 - On demand PNT services during crisis
The success of satellite-enabled PNT applications is accompanied by a growing dependency of our society and economy on the availability of GNSS, which has motivated R&D, demonstration as well as deployment of so-called Alternative PNT systems (APNT).
This dependency extends to GNSS augmentation systems and PNT capabilities relying on local terrestrial infrastructures which can be exposed to…
117 - Resilience Techniques and Algorithms for GNSS Space Receivers in Interference, Jamming and Spoofing Conditions
The threats of ground-based interference, jamming, and spoofing impacting terrestrial and aviation receivers are well recognised by the navigation community, driving many system- level and user-level mitigation studies and technology developments. However, sufficient attention has not yet been given to the impact and mitigation of these threats on GNSS space receivers in LEO (commercial and…
116 - Design of CRPAs for aviation and high-accuracy services
Jamming and spoofing of GNSS are a major concern. A controlled reception pattern antenna (CRPA) is one of the most powerful technologies to mitigate jamming and spoofing. As a result, it is attractive to start using CRPAs for several applications, including aviation and high-accuracy services. However, the use of a specific CRPA for specific applications requires a detailed evaluation of the…
105 - Hybrid black-white-modelling estimation and machine learning algorithms for PNT engines
The application of machine learning (based on “black-box” modelling) is of interest in problems that are difficult to solve based on traditional (optimal) estimators for simple models (“white-box” modelling). Black-box modelling is difficult to be explained and understood (i.e., difficult to understand what to expect in unknown or new situations), involve high computation complexity, and…