  Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to
security-sensitive areas and personal privacy. A UAV can shoot photos at
height, but how to detect such an uninvited intruder is an open problem. This
paper presents mmHawkeye, a passive approach for UAV detection with a COTS
millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of
the type, motions, and flight trajectory of the UAV, while exploiting the
signal feature induced by the UAV's periodic micro-motion (PMM) for long-range
accurate detection. The design is therefore effective in dealing with low-SNR
and uncertain reflected signals from the UAV. mmHawkeye can further track the
UAV's position with dynamic programming and particle filtering, and identify it
with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on
a commercial mmWave radar and evaluate its performance under varied settings.
The experimental results show that mmHawkeye has a detection accuracy of 95.8%
and can realize detection at a range up to 80m.
