Trends and Challenges in Next-Generation GNSS Interference Management
Marata, Leatile; Jaramillo-Civill, Mariona; Imbiriba, Tales; Valisuo, Petri; Kuusniemi, Heidi; Lohan, Elena Simona; Closas, Pau (2026-04-29)
Lataukset:
Marata, Leatile
Jaramillo-Civill, Mariona
Imbiriba, Tales
Valisuo, Petri
Kuusniemi, Heidi
Lohan, Elena Simona
Closas, Pau
29.04.2026
IEEE Aerospace and Electronic Systems Magazine
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202607068173
https://urn.fi/URN:NBN:fi:tuni-202607068173
Kuvaus
Peer reviewed
Tiivistelmä
The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerg ing applications such as autonomous driving and smart environmental monitoring. However, these advancements are accompanied by a rise in interference threats, which can significantly compromise the reliability and safety of GNSS. Such interference problems are typically addressed through signal-processing techniques that rely on physics inspired mathematical models. Unfortunately, solutions of this nature often fail to fully capture different forms of interference such as complex types. To address this, artificial intelligence (AI)-inspired solutions are expected to play a key role in future interference management solutions, thanks to their ability to exploit data in addition to physics-based models. This magazine paper discusses the main challenges and tasks required to secure GNSS receivers and present a research vision on how AI can be leveraged towards achieving more robust GNSS-based positioning.
Kokoelmat
- TUNICRIS-julkaisut [25742]
