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

TL;DR

This paper introduces a comprehensive real-world UAV RF dataset, collected under diverse operational conditions, to facilitate research in RF fingerprinting, model recognition, and interference-aware signal processing.

Contribution

It provides a systematically organized, multi-scenario UAV RF dataset with detailed annotations, enabling structured experimentation and reproducibility in UAV RF signal research.

Findings

Dataset covers 26 UAV units across 8 models.

Includes diverse flight states, altitudes, and speeds.

Supports research in RF fingerprinting and interference analysis.

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.