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

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A new multi-sensor drone dataset combines Frequency-Modulated Continuous Wave (FMCW) radar, Continuous Wave (CW) radar, and Radio Frequency (RF) signals. This resource aids in developing advanced drone detection and classification systems.

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing
  • Artificial Intelligence

Background:

  • The proliferation of drones presents significant security challenges.
  • Existing datasets often lack multi-modal sensor data, hindering comprehensive system development.
  • There is a need for synchronized, multi-sensor data for robust drone detection.

Purpose of the Study:

  • To introduce a novel, time-synchronized, multi-sensor dataset for drone detection and classification.
  • To facilitate the development and evaluation of AI-driven drone security solutions.
  • To enable research into multimodal sensor fusion and distance-aware classification.

Main Methods:

  • Collected time-synchronized signals from FMCW radar, CW radar, and RF receivers.
  • Acquired data from four commercial drones and one non-drone target at various distances (2-30m).
  • Included raw and processed signals (range-Doppler maps, Doppler spectrum, power spectral densities).

Main Results:

  • A comprehensive dataset enabling direct comparison and fusion of multi-sensor signals.
  • Facilitated both signal-level and image-based analysis for drone detection.
  • Provided a reliable resource for evaluating AI-based detection algorithms.

Conclusions:

  • The presented dataset is crucial for advancing drone detection and classification technologies.
  • It supports the development of multimodal sensor-fusion strategies and AI learning models.
  • This resource will accelerate research in drone security and surveillance.