Trends and Challenges in Next-Generation GNSS Interference Management
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912679965229056 |
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| author | Marata, Leatile Jaramillo-Civill, Mariona Imbiriba, Tales Välisuo, Petri Kuusniemi, Heidi Lohan, Elena Simona Closas, Pau |
| author_facet | Marata, Leatile Jaramillo-Civill, Mariona Imbiriba, Tales Välisuo, Petri Kuusniemi, Heidi Lohan, Elena Simona Closas, Pau |
| contents | The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging 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-based mathematical models. Unfortunately, solutions of this nature can often fail to fully capture the complex forms of interference. 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 and present a research vision on how AI can be leveraged towards achieving more robust GNSS-based positioning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trends and Challenges in Next-Generation GNSS Interference Management Marata, Leatile Jaramillo-Civill, Mariona Imbiriba, Tales Välisuo, Petri Kuusniemi, Heidi Lohan, Elena Simona Closas, Pau Signal Processing The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging 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-based mathematical models. Unfortunately, solutions of this nature can often fail to fully capture the complex forms of interference. 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 and present a research vision on how AI can be leveraged towards achieving more robust GNSS-based positioning. |
| title | Trends and Challenges in Next-Generation GNSS Interference Management |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.27576 |