Edge-Aware Thermal Infrared UAV Swarm Tracking

2026-07-14

AI Summary

This work addresses the challenge of efficiently tracking micro unmanned aerial vehicles (UAVs) in thermal infrared imagery, where limited visual features, frequent occlusions, and high maneuverability hinder robust performance, and existing methods often fail to meet the real-time constraints of edge devices. To this end, the authors propose a lightweight online tracking framework tailored for edge deployment, centered on an Adaptive Kinematic Kalman Filter (AKKF). The AKKF innovatively integrates a state-dependent kinematic model into the linear Kalman filter and incorporates transient false-positive suppression alongside a kinematics-driven predictive coasting mechanism. This design enhances robustness against highly dynamic motion and thermal image jitter without significantly increasing computational overhead. Experiments on the BSB benchmark demonstrate that the proposed method achieves a favorable trade-off between tracking accuracy and computational efficiency, offering a new paradigm for real-time, edge-based tracking of thermal UAV swarms.

Abstract

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.

Problem

• Thermal Infrared Tracking
• UAV Swarm
• Edge Computing
• Real-time Tracking
• Kalman Filter

Innovation

• Adaptive Kinematic Kalman Filter
• edge-aware tracking
• thermal infrared UAV swarm
• real-time efficiency
• kinematics-driven predictive coasting