Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types is a work by Jun Yan, Huiping Hu, Jiangkun Gong, Deyong Kong, and Deren Li.

In this study, the authors examine the use of micro-Doppler signals produced by different blade types (i.e., puller and lifting blades) to enhance radar-based target recognition of small drones. Small drones are categorized into three types based on their blade configurations: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades.

To quantify the radar signatures of these three drone types, the authors used statistical measures such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). The findings indicate that the micro-Doppler signals of lifting blades in all three drone types are stronger than those of puller blades. Specifically, pusher blades’ DFD and DMR values were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of puller blades are weaker and more stable than those of lifting blades.

This study demonstrates the potential of using micro-Doppler signatures modulated by different blade types to improve the detection and identification of drone types by drone detection radar.

Publication Date– April 2023

Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types contains the following major sections:

  • Introduction
  • Materials and Methods
  • Results
  • Discussion
  • Conclusions

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Post Images- Photos of the radar and drones (left) and Radar echoes and spectrum of drones- fixed-wing, quad-rotor and VTOL (Post Image Credit: Authors)