Trends and Challenges in Next-Generation GNSS Interference Management

TL;DR

This paper reviews the evolving landscape of GNSS interference management, emphasizing the potential of AI-driven solutions to enhance robustness against complex interference threats in safety-critical applications.

Contribution

It provides a comprehensive overview of current challenges and proposes a research vision for integrating AI techniques into GNSS interference mitigation strategies.

Findings

AI can exploit data beyond physics-based models for better interference detection.

Current physics-based methods may fail against complex interference patterns.

Future research should focus on AI-driven, adaptive interference management solutions.

Abstract

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.