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.
Highlights
Introduction and MotivationOver the last decades, Global Navigation Satellite Systems (GNSS) receiver technologies have significantly evolved
We have provided in open-access a multi- purpose dataset of raw GNSS measurements from sky satellites and Spectracom simulator
We have shown one particular use case of such data, namely how our data can be used for Radio Frequency (RF) fingerprinting problem
Summary
Introduction and MotivationOver the last decades, Global Navigation Satellite Systems (GNSS) receiver technologies have significantly evolved. Many GNSS Software Defined Radio (SDR) solutions are available, where baseband digital representations of raw GNSS data may be used to develop, test, and fine-tune new algorithms. New algorithms relying on raw I/Q GNSS data can serve different purposes, such as enhanced acquisition and tracking solutions or increased resilience (e.g., robustness against interference, multipath, atmosphere effects, interferers such as spoofers and jammers, etc.). Knowledge, no sizeable dataset is currently available It is the main purpose of the authors to provide a set of GNSS SDR raw data with different scenarios in open-access for further testing purposes. The use case that we give as an example in this paper is a sub-set of Radio Frequency Fingerprinting (RFF) problem, namely a signal classification problem, based on transmitter-specific and receiver-specific features
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