Descriptor: Tuni2025 GNSS - Galileo and GPS Spoofing Datasets (TG-GGSD)

Abstract

The Tuni2025 GNSS spoofing datasets provide raw in-phase and quadrature (I/Q) measurement data from Global Navigation Satellite System (GNSS) signals to support research in spoofing detection and signal authentication. It includes a total of 17 scenarios: eight for Galileo signals and nine for GPS signals, featuring static spoofing attacks, both with and without multipath. One GPS scenario includes also a delayed spoofer activation with all PRNs spoofed, allowing for a time-segmented analysis. The spoofer signals are generated in-lab with a Spectracom signal generator, while the genuine GNSS signals are collected from the sky, via a roof-top GNSS antenna.The data was collected using a USRP NI-2954 software-defined radio (SDR) and a NovAtel GNSS-703 antenna, with a high sampling rate of 50 MHz, to allow high-resolution studies. The spoofing signal was generated with a Spectracom GSG-6 signal generator targeting the GPS L1 and Galileo E1 signals. The pseudorandom codes (PRNs) for the spoofed signals were intentionally generated to be distinct from the genuine PRNs visible at the time of the data collection, in order to enable further investigations of hardware-induced impairments and spoofing signal behavior via Radio Frequency Fingerprinting Identification (RFFI) approaches. Our datasets are unique in this aspect, as they include up to five spoofed PRNs co-existing with genuine PRNs and therefore allowing for a ground truth in spoofing PRN detection, unlike few other existing datasets in the literature that superpose fake and genuine signals with the same PRNs.Each scenario is provided as a standalone raw binary file, accompanied by a detailed README file that describes its structure, its PRN configuration, the sampling parameters (sampling frequency and number of quantization bits), and some file interpretation guidelines. No scenario control files or software scripts are included. The dataset is intended for reproducibility in spoofing-detection algorithm testing, RFFI studies, and data-driven modeling of spoofing environments in GNSS systems.