AoI-FusionNet: Age-Aware Tightly Coupled Fusion of UWB-IMU under Sparse Ranging Conditions

TL;DR

AoI-FusionNet is a deep learning framework that fuses UWB and IMU data for accurate 3D motion tracking in challenging environments, effectively handling sparse and stale measurements through an AoI-aware decay and attention mechanisms.

Contribution

This work introduces AoI-FusionNet, a novel tightly coupled fusion approach with AoI-awareness and learned gating, improving localization robustness under sparse and degraded sensing conditions.

Findings

Significantly reduces localization errors in sparse UWB scenarios.

Outperforms traditional multilateration and loose fusion baselines.

Demonstrates robustness with real-world avalanche data.

Abstract

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.