邊緣感知熱紅外無人機集群追蹤

摘要

熱紅外線（TIR）成像在視覺退化環境中對無人機集群操作至關重要。然而，由於外觀線索有限、頻繁遮蔽及快速機動，追蹤微型無人機仍極具挑戰。儘管Anti-UAV挑戰賽等基準的推動取得了顯著進展，現有方法主要優先考慮精確度，卻忽視了即時邊緣部署的計算限制。標準卡爾曼濾波器（KF）具備邊緣設備所需的效率，但其恆速假設在高度動態的無人機運動及熱感應器抖動下常會失效。更複雜的非線性估計器可提升穩健性，但往往增加額外計算成本。為解決此差距，我們提出以自適應運動卡爾曼濾波器（AKKF）為核心的邊緣感知線上追蹤管線，該濾波器在保留即時效率的同時，以狀態相關運動模型增強線性KF。結合瞬態假陽性抑制與運動驅動預測慣性滑行，所提出的管線提升了具挑戰性TIR條件下的軌跡連續性。在超越強基線（BSB）基準上的實驗為邊緣感知無人機追蹤提供了起點，透過聯合評估追蹤性能與計算效率，為未來即時部署提供了見解。

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.