GNSS Signal Correlation and Measurement Datasets for Interference Classification

David Gómez-Casco, Xurxo Otero Villamide, Luciano Musumeci, and Paolo Crosta

Peer Reviewed

Abstract: GNSS datasets are of remarkable importance for the GNSS scientific community to improve the performance of receivers. The aim of this paper is twofold. Firstly, we describe a unique dataset, which contains a large variety of interferences and distortions such as jamming, spoofing, multipath, and Evil WaveForm (EWF). They have been collected from tests with live GNSS signals, except for the EWF signals that have been generated with a GNSS RF simulator. The data is available in different formats such as IQ samples, receiver output files, and correlator outputs. Secondly, we present an example of the application of the dataset based on machine learning classification. The goal is to assess the feasibility of classifying the received signals and the environment of the GNSS receiver by analyzing GNSS observables and correlator outputs from two satellites. The results reveal that machine learning algorithms perform promisingly as they can accurately identify GNSS signals.
Published in: Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024)
September 16 - 20, 2024
Hilton Baltimore Inner Harbor
Baltimore, Maryland
Pages: 2855 - 2864
Cite this article: Gómez-Casco, David, Villamide, Xurxo Otero, Musumeci, Luciano, Crosta, Paolo, "GNSS Signal Correlation and Measurement Datasets for Interference Classification," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 2855-2864. https://doi.org/10.33012/2024.19795
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