Innovative Signal Processing Solutions for Next-Generation Satellite Navigation Systems

This dissertation explores advancements in future navigation satellite systems, proposing and analyzing solutions at system, signals, and user level. The objective of this work has been to seek for performance improvements, acting at various levels of the Global Navigation Satellite System (GNSS) value chain, yet fulfilling possible upcoming needs and constraints. In this context, this work focuses on improving the use of resources, both upstream, to enhance signals and services, and downstream, by leveraging such signals for a better user performance. Specific research questions were addressed for this purpose: how can inter-satellite links (ISLs) be assigned while optimizing data and navigation performance? Can multiple signals transmission be more efficient? How can we leverage signal multiplicity and receiver technologies to improve accuracy and robustness of the final position, velocity, and time (PVT) estimation? The first part focuses on space segment evolution, proposing the use of optical inter-satellite links (OISLs) to enhance satellite autonomy and positioning accuracy. A novel contact plan design scheme, based on a degree-constrained minimum spanning tree (DCMST) heuristic, optimizes inter-satellite link assignments, achieving an 85% improvement in position dilution of precision (PDOP) for the system under study. This solution enhances positioning accuracy and fosters greater space segment autonomy, critical for future large-scale constellations. The second part investigates signal multiplexing methods for future GNSS payloads, addressing power loss challenges in combining multiple signals. An input optimization method for multicarrier multiplexing algorithms is proposed, avoiding poorly performing configurations that cause power losses exceeding 20%. This ensures efficient use of power resources, enabling seamless integration of new signals and supporting rapid reconfigurations to adapt to evolving system requirements. The third part focuses on user-level technologies and signal processing algorithms, leveraging modern receiver capabilities and enhanced connectivity. Two key contributions are made: (i) a meta-signal processing architecture that exploits multiple signals for improved accuracy and Doppler robustness, and (ii) a physics-informed crowdsourcing algorithm for jamming localization. The concept of meta-signal is extended to orthogonal signals, enabling efficient use of band-limited signals while maintaining adaptability. The crowdsourcing algorithm leverages enhanced connectivity to collaboratively detect and localize intentional interference, combining physics-based path loss modeling with data-driven enhancements to localize jammers in diverse scenarios. The growing complexity of satellite constellations and the demand for higher accuracy, robustness, and adaptability necessitate novel approaches. By optimizing resource utilization and leveraging emerging technologies, this work contributes to the ongoing evolution of GNSSs, ensuring its continued relevance and reliability in an increasingly complex framework.