Diffusion-Based Trajectory Restoration for Aerial Vehicle Tracking in Ground-to-Air Remote Sensing Systems

Continuous trajectory maintenance is important for aerial vehicle monitoring, threat assessment, and warning or interception decisions in ground-to-air remote sensing systems. In the considered system, radar and radio-frequency (RF) sensing are used together to produce fused aerial tracks. In practical monitoring, the fused trajectory can still be interrupted by occlusion, maneuvering, missed detections, poor sensing geometry, or unstable measurements. These interruptions produce fragmented tracks and reduce the reliability of long-term surveillance. This paper formulates 3D trajectory restoration as a post-processing task for fused aerial tracks and proposes AeroDiff-TIR, a conditional diffusion-based restoration framework. The method represents trajectories in a local Cartesian coordinate system and treats each interrupted segment as the missing part of a time series. Given the observed points before and after a gap, AeroDiff-TIR learns the conditional distribution of the missing segment and restores it through iterative denoising. Experiments on a simulated aerial vehicle trajectory benchmark and measured unmanned aerial vehicle (UAV) trajectories collected by a ground-to-air monitoring system show that AeroDiff-TIR improves trajectory consistency over the complete restored gap and can support trajectory continuity in aerial monitoring systems.