Mitigation of GNSS Ionospheric Effects Using Statistical Learning-based Self-Adaptiveness to Positioning Environment Conditions, Embedded in GNSS SDR User Equipment

Traditional approach to GNSS position estimation constrains the opportunities for GNSS positioning performance improvements, and development of the GNSS resilient to natural and artificial sources of GNSS performance disruptions and degradations. Here we argue that recent advancements in mathematics, statistics, and computer science may allow for development and utilisation of the Positioning-as-a-Service concept, which detaches position estimation from RF and Base-band segments of a traditional blackbox GNSS receiver, and establishes more related connection with GNSS applications and their requirements. We demonstrate the concept with the scenario og the GNSS ionospheric effects mitigation through utilisation of positioning environment space weather situation awareness at the point of position estimation, as well as with utilisation of the statistical learning-based self-adaptive correction models. The two major contributors to self-adaptiveness of the GNSS position estimation process are deployed effortlessly using Software-Defined Radio (SDR) approach, rendering the GNSS positioning estimation algorithm a computationally distributed feature.