Identifying GNSS signals based on their radio frequency (RF) features—A dataset with GNSS raw signals based on roof antennas and spectracom generator

Ruben Morales Ferre, Wenbo Wang, Alejandro Sanz Abia, Elena Simona Lohan

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification based on front-end hardware characteristics at transmitter or receiver side. Examples are given in this paper of achievable classification accuracy of six of the collected signal classes. The RFF is one of the state-of-the-art, promising methods to identify GNSS transmitters and receivers, and can find future applicability in anti-spoofing and anti-jamming solutions for example. The uses of the provided raw data are not limited to RFF studies, but can extend to uses such as testing GNSS acquisition and tracking, antenna array experiments, and so forth.

Original languageEnglish
Article number18
JournalData
Volume5
Issue number1
DOIs
StatePublished - Mar 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Galileo
  • Global Navigation Satellite Systems (GNSS)
  • Global Positioning Systems (GPS)
  • Machine learning
  • Radio Frequency Fingerprinting (RF FP)
  • Roof antenna
  • Spectracom

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