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A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking

Haolin Zheng, Ning Gao, Zhenghang Zhu, Zhijun Huang, Shi Jin, Michail Matthaiou

Abstract

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.

A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking

Abstract

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.
Paper Structure (7 sections, 3 equations, 4 figures, 1 table)

This paper contains 7 sections, 3 equations, 4 figures, 1 table.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Data Records
  4. Technical Validation
  5. Data Availability
  6. Code Availability
  7. Acknowledgements

Figures (4)

  • Figure 1: Overall hardware architecture of the RF signal acquisition system, including UAVs, SDR-based receiver, data storage device, and auxiliary equipment.
  • Figure 2: Standardized signal acquisition flowchart.
  • Figure 3: Representative STFT spectrogram samples from the proposed dataset under three acquisition scenarios: single-UAV hovering, dual-UAV mixed transmission, and UAV signals mixed with Wi-Fi interference.
  • Figure 4: Confusion matrix for 26 UAV operational states (c1-c26) obtained using a baseline EfficientNetB0 classifier on dataset-1.