AoI-FusionNet: Age-Aware Tightly Coupled Fusion of UWB-IMU under Sparse Ranging Conditions
This work addresses robust localization for applications like avalanche monitoring in challenging environments, representing a domain-specific incremental improvement.
The paper tackles the problem of accurate motion tracking in GNSS-denied outdoor environments by introducing AoI-FusionNet, a tightly coupled deep learning framework that fuses raw UWB and IMU data for 3D trajectory estimation, resulting in substantially reduced mean and tail localization errors under sparse ranging conditions.
Accurate motion tracking of snow particles in avalanche events requires robust localization in global navigation satellite system (GNSS)-denied outdoor environments. This paper introduces AoI-FusionNet, a tightly coupled deep learning-based fusion framework that directly combines raw ultra-wideband (UWB) time-of-flight (ToF) measurements with inertial measurement unit (IMU) data for 3D trajectory estimation. Unlike loose-coupled pipelines based on intermediate trilateration, the proposed approach operates directly on heterogeneous sensor inputs, enabling localization even under insufficient ranging availability. The framework integrates an Age-of-Information (AoI)-aware decay module to reduce the influence of stale UWB ranging measurements and a learned attention gating mechanism that adaptively balances the contribution of UWB and IMU modalities based on measurement availability and temporal freshness. To evaluate robustness under limited data and measurement variability, we apply a diffusion-based residual augmentation strategy during training, producing an augmented variant termed AoI-FusionNet-DGAN. We assess the performance of the proposed model using offline post-processing of real-world measurement data collected in an alpine environment and benchmark it against UWB multilateration and loose-coupled fusion baselines. The results demonstrate that AoI-FusionNet substantially reduces mean and tail localization errors under intermittent and degraded sensing conditions.