采用固态激光雷达的无人机跟踪：动态多频率扫描积分
UAV Tracking with Solid-State Lidars:Dynamic Multi-Frequency Scan Integration
AI总结：
针对复杂环境下无人机跟踪难题，提出基于固态激光雷达的动态多频率扫描积分跟踪方法，融合双频扫描并结合逆协方差交集法与卡尔曼滤波，实现与基线相当且单频场景下更可靠精准的跟踪效果。
摘要：
随着无人机在各行业的应用日益广泛，这类无人飞行器（UAV）在复杂环境（如GNSS拒止环境）中的导航与跟踪已成为关键问题。本文提出了一种基于固态LiDAR的地面UAV跟踪系统新方法，该方法根据与UAV的距离及其飞行速度动态调整LiDAR的帧积分时间。本方法融合两种同步扫描积分频率以实现高精度持续跟踪，即便在复杂场景下也能可靠估计UAV状态。采用逆协方差交集法与卡尔曼滤波器可提升跟踪精度，且能应对复杂跟踪场景。我们开展了多项实验以评估所提跟踪系统的性能并明确其局限性。实验结果表明，该方法的跟踪性能与现有基线方法相当，且在仅有一种频率可用或频率不可靠的情况下，能提供更可靠、更精准的跟踪效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
