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<p>The widespread use of drones across industries has raised concerns about security threats. To provide a publicly available data set for developing robust drone detection and classification systems, we present a multi-sensor dataset composed of time-synchronized signals from Frequency-Modulated Continuous Wave (FMCW) radar, Continuous Wave (CW) radar, and Radio Frequency (RF) receiver. The dataset includes raw and processed signals collected from four different commercial drones and one non-drone target, and signals for different sensors are collected simultaneously. Measurements were taken across distances ranging from 2 to 30 meters in 2-meter intervals, with repeated trials under controlled conditions to ensure consistency. Unlike existing datasets that rely on a single sensing modality, our dataset enables direct comparison and fusion of multi-sensor signals. It contains both raw signals and processed representations—range-Doppler maps, Doppler spectrum, and power spectral densities—facilitating signal-level and image-based analysis. This time-synchronized dataset offers a reliable resource for developing and evaluating multimodal sensor-fusion strategies, distance-aware classification models, and advanced Artificial Intelligence-based detection algorithms, including supervised and self-supervised learning.</p>

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A Time-Synchronized Multi-Sensor drone dataset acquired from multiple radars and RF receiver

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Seung-Kyu Han,
  • Young-Ho Jung

摘要

The widespread use of drones across industries has raised concerns about security threats. To provide a publicly available data set for developing robust drone detection and classification systems, we present a multi-sensor dataset composed of time-synchronized signals from Frequency-Modulated Continuous Wave (FMCW) radar, Continuous Wave (CW) radar, and Radio Frequency (RF) receiver. The dataset includes raw and processed signals collected from four different commercial drones and one non-drone target, and signals for different sensors are collected simultaneously. Measurements were taken across distances ranging from 2 to 30 meters in 2-meter intervals, with repeated trials under controlled conditions to ensure consistency. Unlike existing datasets that rely on a single sensing modality, our dataset enables direct comparison and fusion of multi-sensor signals. It contains both raw signals and processed representations—range-Doppler maps, Doppler spectrum, and power spectral densities—facilitating signal-level and image-based analysis. This time-synchronized dataset offers a reliable resource for developing and evaluating multimodal sensor-fusion strategies, distance-aware classification models, and advanced Artificial Intelligence-based detection algorithms, including supervised and self-supervised learning.