UAV Tracking with Solid-State Lidars: Dynamic Multi-Frequency Scan Integration

With the increasing use of drones across various industries, the navigation and tracking of these unmanned aerial vehicles (UAVs) in challenging environments, namely 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 state estimation of the UAV even in challenging scenarios. The application of the Inverse Covariance Intersection method and Kalman filters allows for better tracking accuracy and can handle challenging tracking scenarios. Compared to previous works in solid-state lidar tracking, this paper presents a more complete and robust solution. We have performed a number of experiments to evaluate the performance of the proposed tracking system and identify its limitations. Our experimental results demonstrate that the proposed method clearly outperforms the baseline method and ensures tracking is more robust across different types of trajectories. The code is open-source and available at github.com/TIERS/dynamic_scan_tracking.