Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints

• URL: http://arxiv.org/abs/2504.04829v1
• Date: Mon, 07 Apr 2025 08:37:18 GMT
• Title: Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints
• Authors: Wenzhong Yan, Feng Yin, Jun Gao, Ao Wang, Yang Tian, Ruizhi Chen,
• Abstract summary: Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space.<n>Existing benchmark methods primarily rely on the measured fingerprints, while neglecting valuable spatial and environmental characteristics.<n>We propose a systematic integration of an Attentional Graph Neural Network (AGNN) model, capable of learning spatial adjacency relationships and aggregating information from neighboring fingerprints.
• Score: 17.159049478569173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fingerprint-based indoor localization is often labor-intensive due to the need for dense grids and repeated measurements across time and space. Maintaining high localization accuracy with extremely sparse fingerprints remains a persistent challenge. Existing benchmark methods primarily rely on the measured fingerprints, while neglecting valuable spatial and environmental characteristics. In this paper, we propose a systematic integration of an Attentional Graph Neural Network (AGNN) model, capable of learning spatial adjacency relationships and aggregating information from neighboring fingerprints, and a meta-learning framework that utilizes datasets with similar environmental characteristics to enhance model training. To minimize the labor required for fingerprint collection, we introduce two novel data augmentation strategies: 1) unlabeled fingerprint augmentation using moving platforms, which enables the semi-supervised AGNN model to incorporate information from unlabeled fingerprints, and 2) synthetic labeled fingerprint augmentation through environmental digital twins, which enhances the meta-learning framework through a practical distribution alignment, which can minimize the feature discrepancy between synthetic and real-world fingerprints effectively. By integrating these novel modules, we propose the Attentional Graph Meta-Learning (AGML) model. This novel model combines the strengths of the AGNN model and the meta-learning framework to address the challenges posed by extremely sparse fingerprints. To validate our approach, we collected multiple datasets from both consumer-grade WiFi devices and professional equipment across diverse environments. Extensive experiments conducted on both synthetic and real-world datasets demonstrate that the AGML model-based localization method consistently outperforms all baseline methods using sparse fingerprints across all evaluated metrics.

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