The potential usage of UAVs in daily life has made monitoring them essential. However, existing systems for monitoring UAVs typically rely on cameras, LiDARs, or radars, whose limited sensing range or high deployment cost hinder large-scale adoption. In response, we develop BSense, the first system that tracks UAVs by leveraging point clouds from commercial 5G-A base stations. The key challenge lies in the dominant number of noise points that closely resemble true UAV points, resulting in a noise-to-UAV ratio over 100:1. Therefore, identifying UAVs from the raw point clouds is like finding a needle in a haystack. To overcome this, we propose a layered framework that filters noise at the point, object, and trajectory levels. At the raw point level, we observe that noise points from different spatial regions exhibit distinguishable and consistent signal fingerprints, which we can model to identify and remove them. At the object level, we design spatial and velocity consistency checks to identify false objects, and further compute confidence scores by aggregating these checks over multiple frames for more reliable discrimination. At the final trajectory level, we propose a Transformer-based network that captures multi-frame motion patterns to filter the few remaining false trajectories. We evaluated BSense on a commercial 5G-A base station deployed in an urban environment. The UAV was instructed to fly along 25 distinct trajectories across 54 cases over 7 days, yielding 155 minutes of data with more than 14,000 frames. On this dataset, our system reduces the number of false detections from an average of 168.05 per frame to 0.04, achieving an average F1 score of 95.56% and a mean localization error of 4.9 m at ranges up to 1,000 m.

翻译：无人机在日常生活中的潜在应用使其监控变得至关重要。然而，现有的无人机监控系统通常依赖于摄像头、激光雷达或雷达，这些系统因感知范围有限或部署成本高昂而阻碍了大规模采用。为此，我们开发了BSense，这是首个通过利用商用5G-A基站点云来追踪无人机的系统。其核心挑战在于噪声点数量占据绝对主导地位，且与真实无人机点极为相似，导致噪声与无人机比例超过100:1。因此，从原始点云中识别无人机如同“大海捞针”。为克服这一难题，我们提出了一种分层框架，在点级、目标级和轨迹级过滤噪声。在原始点级，我们观察到来自不同空间区域的噪声点展现出可区分且一致的信号指纹，可通过建模来识别并去除它们。在目标级，我们设计了空间一致性校验和速度一致性校验来识别虚假目标，并通过多帧聚合这些校验结果计算置信度分数，以实现更可靠的判别。在最终的轨迹级，我们提出了一种基于Transformer的网络，该网络捕捉多帧运动模式以过滤少量残留的虚假轨迹。我们在城市环境中部署的商用5G-A基站上评估了BSense。无人机在7天内按54种情形沿25条不同轨迹飞行，生成了155分钟、超过14000帧的数据。在此数据集上，我们的系统将每帧平均误检数从168.05降至0.04，在高达1000米范围内实现了95.56%的平均F1分数和4.9米的平均定位误差。