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、测绘和运输等多个行业不可或缺的工具。然而，在复杂环境（如杂乱或全球导航卫星系统（GNSS）受限环境）中跟踪无人机的难题仍未解决。此外，无人机正被用作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。本文评估了一种利用固态LiDAR和卡尔曼滤波器（KF）在GNSS受限环境中跟踪无人机的多扫描集成方法的性能。我们测试了该算法在不同距离和速度下于大型开阔区域跟踪无人机的能力。定量分析表明，虽然仅使用恒速模型的“基于检测的跟踪”方法能持续跟踪目标，但利用KF集成多扫描频率的方法能够实现更低的定位误差，是在类似场景中跟踪无人机的可行方案。