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Bending-Aware Spectrogram Correlation for W-Band Quadcopter Detection: A Physics-Informed Non-Coherent Detection
Yael Balal1, Natan Steinmetz2, Arie Sherenzon2
1Department of Electrical and Electronics Engineering, Afeka College of Engineering, Tel Aviv 6910717, Israel.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study introduces a novel W-band radar detection framework for small unmanned aerial vehicles (UAVs) by modeling rotor blade flexibility. The new method enhances detection accuracy for UAVs in challenging environments.
Area of Science:
- Radar Systems Engineering
- Aerospace Engineering
- Signal Processing
Background:
- Small multi-rotor UAVs present significant radar detection challenges due to low, aspect-dependent radar cross-section and Doppler overlap with clutter.
- Existing detection methods struggle with UAV signatures, especially in complex environments with birds and ground clutter.
Purpose of the Study:
- To develop a physics-informed, non-coherent radar detection framework for W-band (94 GHz) sensing of small UAVs.
- To exploit the physics of blade flexibility for improved UAV detection and characterization.
Main Methods:
- Developed a compact 3D micro-Doppler model incorporating counter-rotating rotor kinematics and blade bending.
- Utilized cosine similarity on magnitude spectrograms for robust detection against oscillator impairments.
- Validated the model against measured W-band data, demonstrating time-frequency periodicity.
Main Results:
- The detector achieves high probability of detection (Pd≈0.9) at low signal-to-noise ratios (SNR ≈-14 dB) with a calibrated false-alarm rate (PFA=0.01).
- The framework demonstrates robustness to carrier-frequency offsets and phase random walk, outperforming traditional matched filters.
- Effective detection is maintained against simulated bird and clutter interference, outperforming energy detectors.
Conclusions:
- The proposed physics-informed framework offers an interpretable, training-free baseline for phase-limited W-band UAV sensing.
- Exploiting blade flexibility significantly enhances UAV detection capabilities in challenging radar environments.
- This approach provides a robust solution for identifying small UAVs even with imperfect radar hardware.
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