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Boundary conditions for snapshot-based spoofing detection using OSNMA unpredictable symbols

Husnain Shahid, Daniel Egea-Roca, Luca Canzian, Carlo Sarto, Oscar Pozzobon, J. Reyes-González, Gonzalo Seco Granados, José Antonio López Salcedo

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Anti-spoofing techniques for Global Navigation Satellite System (GNSS) receivers are garnering growing interest as crucial facilitators for the deployment of GNSS-based applications and services. To this end, Galileo is providing the Galileo Open Service Navigation Message Authentication (OSNMA), which conveys a set of cryptographic data with the purpose of authenticating the content of the Galileo I/NAV message. Some of these data are unpredictable to the users, and therefore cannot be known in advance and used to generate a counterfeit signal, thus introducing an additional protection level against potential spoofers. The purpose of this paper is to discuss the boundary conditions that make possible the use of such unpredictable symbols for spoofing detection. The proposed technique is referred to as snapshot OSNMA and it is envisaged as a client-server architecture whereby the user gathers a snapshot of the Galileo E1-B signal, extracts a few unpredictable symbols and sends them to a remote server where their authenticity is analyzed and reported back to the user. The problem is formulated using an equivalent binary symmetric channel (BSC), and results show that spoofing detection is possible provided that certain boundary conditions are fulfilled. The proposed technique thus becomes a valuable candidate for the exploitation of OSNMA in snapshot GNSS receivers where neither continuous processing of the GNSS signal nor OSNMA-enabled capabilities are implemented.
Original languageEnglish
Article number27
Number of pages16
JournalGPS Solutions
Volume30
Issue number1
DOIs
Publication statusPublished - 19 Nov 2025

Keywords

  • Galileo
  • GNSS
  • Spoofing
  • OSNMA
  • Snapshot receiver
  • Symbol unpredictability

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