A Geomagnetic Ambient-Aware GNSS Ionospheric Delay Correction Model Improves GNSS PNT Performance for Urban Science Applications

Global Navigation Satellite Systems (GNSSs) have become a fundamental technology supporting modern civilisation, industry, and society, including a vast number of applications in urban science. Degradations and disruptions in GNSS Positioning, Navigation, and Timing (PNT) service performance caused by both natural and adversarial sources degrade the quality and robustness of GNSS-based applications. Ionospheric effects form the principal single source of GNSS PNT performance degradation. Traditional GNSS ionospheric delay correction models cannot resolve the ionospheric effect mitigation problem due to their global nature and an intrinsic lack of agility and flexibility. Here, we contribute to the problem resolution by proposing a geomagnetic ambient-aware GNSS ionospheric delay correction model based on near-real-time measurements of geomagnetic field components as descriptors of the GNSS positioning environment immediately surrounding a GNSS receiver. The proposed geomagnetic ambient-aware GNSS ionospheric delay correction model is developed using statistical/machine learning model-development methods applied to a massive database of related experimental observations. The model may be implemented in traditional single-unit and distributed architectures, such as the previously introduced Ambient-Aware Application-Aligned (AA)2 GNSS PNT framework. It improves GNSS PNT performance for urban science applications, thus increasing their Quality of Service. The proposed approach and the resulting bespoke geomagnetic ambient-aware GNSS ionospheric delay correction model are successfully demonstrated using the R environment for statistical computing for the case of a single-frequency commercial-grade GNSS receiver operating in a sub-equatorial region.