边缘感知热红外无人机集群跟踪

摘要

热红外（TIR）成像技术对于无人机集群在视觉退化环境中的操作至关重要。然而，由于外观特征有限、频繁遮挡以及快速机动，微型无人机的跟踪仍然面临挑战。尽管以Anti-UAV挑战赛等基准为驱动的技术已取得显著进展，现有方法主要侧重于精度，却忽视了实时边缘部署的计算约束。标准卡尔曼滤波（KF）具备边缘设备所需的效率，但其恒速假设在无人机高度动态运动和热传感器抖动下常会失效。更复杂的非线性估计器可提升鲁棒性，但往往会引入额外计算成本。为填补这一空白，我们提出了一种以自适应运动学卡尔曼滤波（AKKF）为核心的边缘感知在线跟踪流水线，该滤波器在保持实时效率的同时，通过状态相关的运动学建模对线性KF进行增强。结合瞬态假阳性抑制与运动学驱动的预测滑行，所提流水线在具挑战性的热红外条件下改善了轨迹连续性。在超越强基线（BSB）基准上的实验通过联合评估跟踪性能与计算效率，为边缘感知的无人机跟踪提供了起点，并为未来实时部署提供了见解。

English

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.