Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator
ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, Finland
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Data 2020, 5(1), 18; https://doi.org/10.3390/data5010018
Received: 6 January 2020 / Revised: 7 February 2020 / Accepted: 13 February 2020 / Published: 17 February 2020
(This article belongs to the Special Issue Data from Smartphone and Wearables)
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.
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Keywords:
Global Navigation Satellite Systems (GNSS); Radio Frequency Fingerprinting (RF FP); spectracom; roof antenna; Galileo; Global Positioning Systems (GPS); machine learning
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MDPI and ACS Style
Morales-Ferre, R.; Wang, W.; Sanz-Abia, A.; Lohan, E.-S. Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator. Data 2020, 5, 18.
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