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.

翻译：摘要：随着无人机在各行业的广泛应用，如何在复杂环境（如全球导航卫星系统拒止环境）中实现对这些无人飞行器的导航与追踪已成为关键问题。本文提出一种基于固态激光雷达的地面无人机追踪系统新方法，该方法根据无人机距离与速度动态调整激光雷达帧积分时间。通过融合两种同步扫描积分频率，系统可同时实现高精度追踪与持续性观测，即使在挑战性场景中仍能可靠估计无人机状态。采用逆协方差交集方法与卡尔曼滤波器，提升了追踪精度并有效应对复杂追踪场景。我们通过系列实验评估了所提追踪系统的性能并识别其局限性。实验结果表明，本文方法在达到与基线方法相当追踪性能的同时，当仅有一种频率可用或不可靠时，能提供更可靠且精确的追踪效果。