Cooperative Detection and Localization of Interference Sources in GNSS

Dear Colleagues,

Today, GNSS user communities have reached full awareness of the danger that GNSS signal interference, either jamming or spoofing, or both, may cause on their sectors, challenging quality and continuity of service, productivity, economical revenues, but also reliability and safety. The number of reported real-world incidents related to GNSS interference at international airports, on highway systems, in metropolitan areas, and more is increasing, as well as the perception of the need for means to block and prevent them.

Signal jamming, either intentional (“informed”) or not, may cause disruption of the GNSS-based service for a single user or over a certain area, while spoofing is a more subtle threat, because it aims at fooling the victim GNSS receiver, taking control of the position or time information it produces. Sometimes the victim is unaware of the attack; other times, the victim coincides with the spoofer, for example, when they can obtain economical revenues from cheating its own PNT information. Nonetheless, the last ten years of R&D have demonstrated that in all the cases, interference attacks leave traces into the receiver, more or less evident and recognizable. Such traces, or clues, once detected in the receiver observables, give the chance of transforming a feared vulnerability into a potential resilience if these clues are opportunely exploited.

However, detection is only the first step in managing the risk associated to GNSS interference. Once the attack is set up, thee ways are then possible: The receiver warns the user and stops providing the positioning and timing service; the receiver attempts to mitigate the effects of the attack with ad hoc signal processing; a user level system or service attempts to neutralize the cause of the interference, locating the interference source and blocking it, even with law enforcement actions. In the first and second way, the state of the art offers several solutions, able to detect and mitigate various types of attacks using signal processing capabilities residing in the receiver itself, or in collaboration with other on-board sensors, typically inertial units. The third way brings a different perspective: cooperation among several receivers subject to the same attack not only allows more reliably detecting the event and issuing early warnings but can also enable procedures of source localization. In this perspective, GNSS receivers act as remote and distributed sensors in a network of peers, which improves resilience through detection and localization of interference sources.

This Special Issue proposes to gather examples of the most recent advances and results of systems and techniques to locate sources of GNSS interference, using GNSS receivers but possibly also other sensors in a collaborative way, exploiting connectivity, data crowd-sourcing, and network processing in distributed or centralized architectures. Detection and localization architectures may use traditional or innovative location means, including, for example, drones. The concept of interference broadly encompasses here all the types of GNSS jamming and spoofing. Of major interest is the discussion of solid results of on-field experiments or hardware-in-the-loop emulations in realistic conditions, including theoretical analyses and derivations.

Perspective papers are invited to cover the following topics, although they should not be limited to this list:

• Collaborative interference detection and classification;
• Collaborative interference geolocation;
• Network-based resilience to GNSS interference;
• Centralized or distributed architectures to geolocate interference source;
• Techniques based on time-difference-of-arrival (TDOA), power-difference-of-arrival (PDOA), or angle of arrival (AOA) to find interference sources.

Dr. Emanuela Falletti
Assoc. Prof. Dr. Laura Ruotsalainen
Guest Editors

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• GNSS interference
• GNSS Jamming
• GNSS Spoofing
• Detection
• Source geolocation
• Collaborative processing
• Crowd sourcing