发表机构
The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在视觉退化环境中热红外无人机群跟踪难题，提出以自适应运动卡尔曼滤波器为中心的边缘感知在线跟踪管道，结合相关技术提升轨迹连续性，通过实验联合评估性能与效率，为实时部署提供见解。
AI 中文摘要
热红外（TIR）成像对于在视觉退化环境中的无人机群操作至关重要。然而，由于外观线索有限、频繁遮挡和快速机动，跟踪小型无人机仍然具有挑战性。尽管有像反无人机挑战这样的基准推动了显著进展，但现有方法主要优先考虑准确性，而忽略了实时边缘部署的计算限制。标准卡尔曼滤波器（KF）提供了边缘设备所需的效率，但其匀速假设在高度动态的无人机运动和热传感器抖动下常常失效。更复杂的非线性估计器可以提高鲁棒性，但通常会引入额外的计算成本。为了解决这一差距，我们提出了一种以自适应运动卡尔曼滤波器（AKKF）为中心的边缘感知在线跟踪管道，它在保持实时效率的同时，通过状态相关的运动建模增强了线性KF。结合瞬态误报抑制和运动学驱动的预测滑行，该管道在具有挑战性的TIR条件下提高了轨迹连续性。在超越强基线（BSB）基准上的实验通过联合评估跟踪性能和计算效率，为边缘感知无人机跟踪提供了一个起点，为未来的实时部署提供了见解。
英文摘要
Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for edge devices, yet its constant-velocity assumption often breaks down under highly dynamic UAV motion and thermal sensor jitter. More sophisticated nonlinear estimators can improve robustness but often introduce additional computational costs. To address this gap, we propose an edge-aware online tracking pipeline centered on the Adaptive Kinematic Kalman Filter (AKKF), which augments the linear KF with state-dependent kinematic modeling while preserving real-time efficiency. Combined with transient false-positive suppression and kinematics-driven predictive coasting, the presented pipeline improves trajectory continuity under challenging TIR conditions. Experiments on the Beyond Strong Baseline (BSB) benchmark provide a starting point for edge-aware UAV tracking by jointly evaluating tracking performance and computational efficiency, offering insights toward future real-time deployment.
Comments7 pages, 4 figures, 3 tables
