融合传感器数据与图神经网络，用少量样本快速适应新环境定位
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion
- 用动态边构建的图网络捕捉信号点空间关系
- 数据融合使定位误差降低15.92%，图网络比传统模型提升30.89%
- 元学习只需少量数据即可适配新场景，适合物联网定位应用
由于无线信号环境变化和数据有限，精准室内定位仍具挑战。本文提出MetaGraphLoc，结合传感器融合、图神经网络（GNN）与元学习，以克服上述限制。该系统融合接收信号强度指示（RSSI）与惯性测量单元（IMU）数据，提升定位精度。所提出的GNN架构采用动态边构造（DEC），捕捉接入点间的空间关系及底层数据模式。通过元学习框架，模型可仅用少量数据快速适应新环境，显著减少校准成本。大量实验表明：数据融合使定位误差降低15.92%；含DEC的GNN相比传统深度神经网络最高提升30.89%准确率；元学习策略有效降低新环境所需数据量。这些进展使MetaGraphLoc成为未来物联网中高精度定位的有力候选方案。
原文摘要 · Abstract (English)
Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received signal strength indicator measurements with inertial measurement unit data to enhance localization accuracy. Our proposed GNN architecture, featuring dynamic edge construction (DEC), captures the spatial relationships between access points and underlying data patterns. MetaGraphLoc employs a meta-learning framework to adapt the GNN model to new environments with minimal data collection, significantly reducing calibration efforts. Extensive evaluations demonstrate the effectiveness of MetaGraphLoc. Data fusion reduces localization error by 15.92%, underscoring its importance. The GNN with DEC outperforms traditional deep neural networks by up to 30.89%, considering accuracy. Furthermore, the meta-learning approach enables efficient adaptation to new environments, minimizing data collection requirements. These advancements position MetaGraphLoc as a promising solution for indoor localization, paving the way for improved navigation and location-based services in the ever-evolving Internet of Things networks.
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