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.

翻译：小型无人机正对安全敏感区域和个人隐私构成潜在威胁。无人机可在高空拍摄照片，但如何检测此类不速之客仍是一个开放性问题。本文提出mmHawkeye——一种基于商用毫米波雷达的被动式无人机探测方法。该方法无需预先获知无人机的类型、运动状态及飞行轨迹，通过利用无人机周期性微动引发的信号特征实现远距离精准检测。该设计能有效应对无人机反射信号的信噪比低及不确定性等问题。mmHawkeye进一步采用动态规划与粒子滤波追踪无人机位置，并通过基于长短期记忆网络的检测器进行身份识别。我们在商用毫米波雷达上实现了mmHawkeye系统，并在多种场景下评估其性能。实验结果表明，mmHawkeye的检测准确率达95.8%，最远探测距离可达80米。