Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator
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 hardw...
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doaj-92952d170a184a4484c8b0cdaf8ff75e2020-11-25T02:32:13ZengMDPI AGData2306-57292020-02-01511810.3390/data5010018data5010018Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom GeneratorRuben Morales Ferre0Wenbo Wang1Alejandro Sanz Abia2Elena Simona Lohan3ITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, FinlandITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, FinlandITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, FinlandITC Faculty, Department of Electrical Engineering, Tampere University, 33720 Tampere, FinlandThis 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.https://www.mdpi.com/2306-5729/5/1/18global navigation satellite systems (gnss)radio frequency fingerprinting (rf fp)spectracomroof antennagalileoglobal positioning systems (gps)machine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ruben Morales Ferre Wenbo Wang Alejandro Sanz Abia Elena Simona Lohan |
spellingShingle |
Ruben Morales Ferre Wenbo Wang Alejandro Sanz Abia Elena Simona Lohan Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator Data global navigation satellite systems (gnss) radio frequency fingerprinting (rf fp) spectracom roof antenna galileo global positioning systems (gps) machine learning |
author_facet |
Ruben Morales Ferre Wenbo Wang Alejandro Sanz Abia Elena Simona Lohan |
author_sort |
Ruben Morales Ferre |
title |
Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator |
title_short |
Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator |
title_full |
Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator |
title_fullStr |
Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator |
title_full_unstemmed |
Identifying GNSS Signals Based on their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator |
title_sort |
identifying gnss signals based on their radio frequency (rf) features—a dataset with gnss raw signals based on roof antennas and spectracom generator |
publisher |
MDPI AG |
series |
Data |
issn |
2306-5729 |
publishDate |
2020-02-01 |
description |
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. |
topic |
global navigation satellite systems (gnss) radio frequency fingerprinting (rf fp) spectracom roof antenna galileo global positioning systems (gps) machine learning |
url |
https://www.mdpi.com/2306-5729/5/1/18 |
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