  Unmanned aerial vehicles (UAVs) are widely used due to their low cost and
versatility, but they also pose security and privacy threats. Therefore,
reliable detection for low-altitude UAVs is an important issue. The strong
ground clutter makes the radar echoes from small UAVs submerged in noise,
resulting in low radar detection reliability. A low-altitude UAV detection
method based on deep contrastive learning is proposed to address the above
problems: Concretely, a low-altitude UAV radar echo model under low-altitude
clutter interference is first established. Based on the echo components and the
UAV Doppler domain identifiable mechanism, a time-frequency transformation
method combining ZAM transform and morphological operations is used to suppress
the ambiguity problem under clutter. Then feature extraction and fusion method
introducing contrast learning is utilized to suppress non-target ground clutter
interference. Finally, a detector relying on semantic features is designed for
the reliable identification of low-altitude UAVs. The experiments carried out
on both real and simulated data confirm that the proposed method can
effectively suppress ground clutter and reliably extract recognizable semantic
features of UAVs. The proposed method achieves lower false and missing alarms
compared with recent state-of-art solutions and improves the detection accuracy
by more than 5% for the same signal-to-noise ratio, which effectively improves
the detection reliability.
