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 and Elena-Simona Lohan
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Ruben Morales-Ferre: ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland
Wenbo Wang: ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland
Alejandro Sanz-Abia: ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland
Elena-Simona Lohan: ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland

Data, 2020, vol. 5, issue 1, 1-13

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.

Keywords: Global Navigation Satellite Systems (GNSS); Radio Frequency Fingerprinting (RF FP); spectracom; roof antenna; Galileo; Global Positioning Systems (GPS); machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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