Pregled bibliografske jedinice broj: 1238848
Mitigation of GNSS Ionospheric Effects Using Statistical Learning-based Self-Adaptiveness to Positioning Environment Conditions, Embedded in GNSS SDR User Equipment
Mitigation of GNSS Ionospheric Effects Using Statistical Learning-based Self-Adaptiveness to Positioning Environment Conditions, Embedded in GNSS SDR User Equipment // Presentations made at the United Nations International Meeting on the Applications of Global Navigation Satellite Systems VIENNA, AUSTRIA, 5 - 9 DECEMBER 2022 / Gadimova, Sharafat ; Gindler, Patrick (ur.).
Beč: UN OOSA, 2022. 26, 16 (pozvano predavanje, međunarodna recenzija, pp prezentacija, znanstveni)
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Naslov
Mitigation of GNSS Ionospheric Effects Using
Statistical Learning-based Self-Adaptiveness to
Positioning Environment Conditions, Embedded in
GNSS SDR User Equipment
Autori
Filjar, Renato
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, pp prezentacija, znanstveni
Izvornik
Presentations made at the United Nations International Meeting on the Applications of Global Navigation Satellite Systems VIENNA, AUSTRIA, 5 - 9 DECEMBER 2022
/ Gadimova, Sharafat ; Gindler, Patrick - Beč : UN OOSA, 2022
Skup
United Nations International Meeting on the Applications of GNSS
Mjesto i datum
Beč, Austrija, 05.12.2022. - 12.12.2022
Vrsta sudjelovanja
Pozvano predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
GNSS ionospheric effects ; statistical learning ; mitigation ; self-adaptiveness ; SDR ; positioning environment conditions awareness
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Elektrotehnika, Računarstvo, Zrakoplovstvo, raketna i svemirska tehnika
