Many critical applications highly depend on Global Navigation Satellite Systems (GNSS) for precise and continuously available positioning and timing information. To warn a GNSS user that the signals are compromised, real-time interference detection is required. Additionally, real-time classification of the interference signal allows the user to select the most effective mitigation methods for the encountered disturbance. A compact proof of concept has been built using commercial off-the-shelf (COTS) components to analyse the jamming detection and classification techniques. It continuously monitors GNSS frequency bands and generates warnings to the user when interference is detected and classified. Various signal spectrum analyses, consisting of kurtosis and power spectral density (PSD) calculations, as well as a machine learning model, are used to detect and classify anomalies in the incoming signals. The system has been tested by making use of a COTS GNSS signal simulator. The simulator is used to generate the upper L-band GNSS signals and different types of interferences. Successful detection and classification is demonstrated, even for interference power levels that do not degrade the performance of a commercial reference receiver.

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is an evolution of the currently used aviation-focused Global Navigation Satellite System integrity service, Receiver Autonomous Integrity Monitoring (RAIM). Where RAIM supports only lateral navigation, with its adaptations including multiple frequencies and constellations, and with the use of Integrity Support Messages (ISMs), ARAIM also supports vertical guidance. Although these techniques were designed to serve the aviation community, ARAIM could be used in a wide range of applications, especially safety-critical applications. With further evolutions, ARAIM could also be extended to cover more demanding applications in various sectors. This work reports the outcomes of the study of the applicability of ARAIM for the Unmanned Aerial Vehicle (UAV) sector.

Resilience against signal disturbances is an important characteristic of GNSS-based PNT solutions. In particular, for critical infrastructures, failure to provide correct PNT information in these domains may have a major societal impact. The Resilience Framework by the Department of Homeland Security (DHS) provides a set of requirements and guidelines to design a PNT solution of a certain level of resilience. Over the lifetime of the applications, it will be of prime importance to assess the resilience of the PNT solutions on a regular basis. Given how often GNSS-based solutions are being applied, partly automating the assessment process will be needed to make this task feasible. To automate the generative process, a machine-readable structure with well-established meaning is required. In this work, the use of fault trees as a formal system to encode the resilience framework is investigated.