  With the increasing use of drones across various industries, the navigation
and tracking of these unmanned aerial vehicles (UAVs) in challenging
environments (such as GNSS-denied environments) have become critical issues. In
this paper, we propose a novel method for a ground-based UAV tracking system
using a solid-state LiDAR, which dynamically adjusts the LiDAR frame
integration time based on the distance to the UAV and its speed. Our method
fuses two simultaneous scan integration frequencies for high accuracy and
persistent tracking, enabling reliable estimates of the UAV state even in
challenging scenarios. The use of the Inverse Covariance Intersection method
and Kalman filters allow for better tracking accuracy and can handle
challenging tracking scenarios.
  We have performed a number of experiments for evaluating the performance of
the proposed tracking system and identifying its limitations. Our experimental
results demonstrate that the proposed method achieves comparable tracking
performance to the established baseline method, while also providing more
reliable and accurate tracking when only one of the frequencies is available or
unreliable.
