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

2026-03-13

AI Summary

This work addresses the challenge of achieving high-precision 3D trajectory estimation in GNSS-denied avalanche-prone outdoor environments, where sparse UWB ranging measurements are insufficient on their own. The authors propose a tightly coupled deep learning framework that directly fuses raw UWB time-of-flight and IMU data. A key innovation is the introduction of an Age-of-Information (AoI)-aware decay module that dynamically suppresses stale ranging measurements, complemented by an attention-based gating mechanism for adaptive multimodal fusion. To enhance robustness under data scarcity, the framework further incorporates diffusion-based generative data augmentation. Experimental results in real-world alpine settings demonstrate that the proposed method significantly reduces both average and tail localization errors, outperforming conventional UWB multilateration and loosely coupled fusion approaches.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• UWB-IMU fusion
• sparse ranging
• GNSS-denied localization
• 3D trajectory estimation
• Age-of-Information

Innovation

• Age-of-Information
• Tightly Coupled Fusion
• UWB-IMU Integration
• Attention Gating
• Diffusion-based Augmentation