A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910035720798208 |
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| author | Zheng, Haolin Gao, Ning Zhu, Zhenghang Huang, Zhijun Jin, Shi Matthaiou, Michail |
| author_facet | Zheng, Haolin Gao, Ning Zhu, Zhenghang Huang, Zhijun Jin, Shi Matthaiou, Michail |
| contents | We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2, which is collected using a dedicated acquisition platform under diverse operational conditions. All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings. The dataset is systematically organized into seven well-defined subsets corresponding to different operational and signal composition scenarios to facilitate structured experimentation. Each file follows a clearly annotated naming convention to enable convenient data indexing and reproducible analysis. The dataset contains RF recordings from 26 UAV units spanning 8 distinct models, captured across varying flight states, altitudes, speeds, acquisition days, and receiver configurations. By covering diverse acquisition settings and signal compositions, the dataset provides a comprehensive resource for future UAV RF signal research, including RF fingerprinting (RFF) identification, model-level recognition, flight state analysis, time-varying RFF study, and interference-aware signal processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00106 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking Zheng, Haolin Gao, Ning Zhu, Zhenghang Huang, Zhijun Jin, Shi Matthaiou, Michail Signal Processing Cryptography and Security We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2, which is collected using a dedicated acquisition platform under diverse operational conditions. All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings. The dataset is systematically organized into seven well-defined subsets corresponding to different operational and signal composition scenarios to facilitate structured experimentation. Each file follows a clearly annotated naming convention to enable convenient data indexing and reproducible analysis. The dataset contains RF recordings from 26 UAV units spanning 8 distinct models, captured across varying flight states, altitudes, speeds, acquisition days, and receiver configurations. By covering diverse acquisition settings and signal compositions, the dataset provides a comprehensive resource for future UAV RF signal research, including RF fingerprinting (RFF) identification, model-level recognition, flight state analysis, time-varying RFF study, and interference-aware signal processing. |
| title | A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking |
| topic | Signal Processing Cryptography and Security |
| url | https://arxiv.org/abs/2603.00106 |