  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.
