Drones have become essential tools in a wide range of industries, including agriculture, surveying, and transportation. However, tracking unmanned aerial vehicles (UAVs) in challenging environments, such cluttered or GNSS-denied environments, remains a critical issue. Additionally, UAVs are being deployed as part of multi-robot systems, where tracking their position can be essential for relative state estimation. In this paper, we evaluate the performance of a multi-scan integration method for tracking UAVs in GNSS-denied environments using a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm's ability to track a UAV in a large open area at various distances and speeds. Our quantitative analysis shows that while "tracking by detection" using a constant velocity model is the only method that consistently tracks the target, integrating multiple scan frequencies using a KF achieves lower position errors and represents a viable option for tracking UAVs in similar scenarios.

翻译：无人机已成为农业、测量和交通等众多行业不可或缺的工具。然而，在复杂环境（如杂乱环境或全球导航卫星系统拒止环境）中跟踪无人机仍然是一个关键问题。此外，无人机正被部署为多机器人系统的一部分，在这些系统中，跟踪其位置对于相对状态估计至关重要。本文评估了一种多扫描融合方法在基于固态激光雷达和卡尔曼滤波的全球导航卫星系统拒止环境中跟踪无人机的性能。我们评估了该算法在不同距离和速度下在大型开阔区域跟踪无人机的表现。定量分析表明，虽然使用恒定速度模型的“基于检测的跟踪”是唯一能够持续跟踪目标的方法，但使用卡尔曼滤波融合多种扫描频率的方法能实现更低的位置误差，是类似场景中跟踪无人机的可行选择。