GNSS Dataset (with Interference and Spoofing) Part IV

Published: 28 May 2024| Version 1 | DOI: 10.17632/jxxcyknzwb.1
Xiaoyan Wang


GNSS Dataset (with Interference and Spoofing) Part X GNSS Dataset (with Interference and Spoofing) consists of three parts: Part I (Raw data of 2023 12 to 20, Sept, 2023, clean data), Part II (Raw data of 2023 21 to 30, Sept, 2023, clean data) and Part III (Processed data 12 to 30, Sept, 2023; data collected with spoofing and jamming on 21 Dec, 2023; Scripts and Material) . Part IV (Supplementary Raw data and Processed data, 15 to 24, March, 2024). Part V (Supplementary Raw data and Processed data, 24 to 30, March, 2024). Part I, II, and III are provided in detail by the paper entitled "GNSS Interference and Spoofing Dataset" in Elsevier, and IV and V use the same collection method supplemented with more spoofing data. The data were recorded by a GNSS receiver installed on the 5th floor of the Science Hall of Yunnan University. HackRF One emits spoofing signals and the commercial jammer emits suppression jamming to attack the receiver. The provided datasets are interesting for the GNSS monitoring, GNSS security, anti-jamming and anti-spoofing mechanisms based scientific communities. These data provide the most comprehensive information available on the spatial and temporal patterns of GNSS satellites, observation and receiver parameters, referring to five constellations (GPS, Campass, Galileo, GLONASS and QZSS) and eight signal bands (L1C/A, L2C, E1, E5b, B1, B2, L1, L2). The dataset provides observations of the receiver three scenarios: normal state, affected by commercial jammers, and spoofed by SDR HackRF One. These observations include more details such as carrier-to-noise density ratio (C/N0), signal spectrum, Doppler shift, pseudorange, carrier phase, satellite health indicator, real-time position data and dilution of precision (DOP), etc. These data can be used to analyze navigation satellite operation rules, satellite covering time above the receiver, satellite overhead time prediction and GNSS monitoring system construction, provides a large amount of fine-grained data that can be used as an example to study safeguards at civil aviation airports, monitoring for harmful radio interference.



Yunnan University


Signal Processing