用动态图结构提升手机端室内定位精度，有效应对设备差异和信号噪声。
GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices
- 构建实时动态图，融合空间关系与信号特性，突破传统方法局限。
- 在多场景测试中定位误差降低1.6至4.72倍，最坏情况误差降1.85至4.57倍。
- 适合移动嵌入式设备部署，尤其适用于复杂高密度信号环境。
精准的室内定位对智能环境中的空间上下文和导航系统至关重要。基于Wi-Fi接收信号强度（RSS）指纹的方法因其兼容移动嵌入式设备而被广泛使用。深度学习模型通过学习不同位置的RSS变化提升了定位精度，但其假设指纹向量存在于欧氏空间，未能考虑空间关系及真实世界中非均匀分布的信号噪声，导致在异构移动设备间泛化能力差，因硬件和信号处理差异造成RSS读数失真。图神经网络（GNN）可通过将室内位置建模为节点、空间与信号关系建模为边，改进传统深度学习模型。然而，现有GNN难以处理非欧氏噪声分布，且存在“图神经网络盲区”问题，在接入点密集环境中性能下降。为此，我们提出GATE框架，通过自适应构建指纹向量的图表示，在保持室内状态空间拓扑的同时，建模非欧氏噪声结构以缓解环境噪声并解决设备异质性问题。GATE引入三项创新：1）注意力超空间向量（AHV），增强消息传递；2）多维超空间向量（MDHV），缓解图神经网络盲区；3）实时边构建（RTEC）方法，实现动态图适应。在多个真实室内场景的评估中，涵盖不同路径长度、接入点密度和异构设备，GATE相比现有最优定位框架，平均定位误差降低1.6至4.72倍，最坏情况误差降低1.85至4.57倍。
原文摘要 · Abstract (English)
Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely used indoor localization approach due to its compatibility with mobile embedded devices. Deep Learning (DL) models improve accuracy in localization tasks by learning RSS variations across locations, but they assume fingerprint vectors exist in a Euclidean space, failing to incorporate spatial relationships and the non-uniform distribution of real-world RSS noise. This results in poor generalization across heterogeneous mobile devices, where variations in hardware and signal processing distort RSS readings. Graph Neural Networks (GNNs) can improve upon conventional DL models by encoding indoor locations as nodes and modeling their spatial and signal relationships as edges. However, GNNs struggle with non-Euclidean noise distributions and suffer from the GNN blind spot problem, leading to degraded accuracy in environments with dense access points (APs). To address these challenges, we propose GATE, a novel framework that constructs an adaptive graph representation of fingerprint vectors while preserving an indoor state-space topology, modeling the non-Euclidean structure of RSS noise to mitigate environmental noise and address device heterogeneity. GATE introduces 1) a novel Attention Hyperspace Vector (AHV) for enhanced message passing, 2) a novel Multi-Dimensional Hyperspace Vector (MDHV) to mitigate the GNN blind spot, and 3) an new Real-Time Edge Construction (RTEC) approach for dynamic graph adaptation. Extensive real-world evaluations across multiple indoor spaces with varying path lengths, AP densities, and heterogeneous devices demonstrate that GATE achieves 1.6x to 4.72x lower mean localization errors and 1.85x to 4.57x lower worst-case errors compared to state-of-the-art indoor localization frameworks.
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