仅用Wi-Fi信号轨迹实现无建筑信息的楼层分离
Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories
- 构建轨迹图，用节点嵌入和聚类识别楼层结构
- 在多个数据集上达到高精度，无需事先知道楼层数
- 适合缺乏建筑信息的室内定位场景
垂直定位，尤其是楼层分离，仍是GPS缺失的多层环境中室内定位系统的主要挑战。本文提出一种完全数据驱动的图基框架，仅使用Wi-Fi指纹轨迹实现盲式楼层分离，无需预先的建筑信息或楼层数量知识。方法将Wi-Fi指纹表示为轨迹图中的节点，边同时捕捉信号相似性和序列运动上下文。通过Node2Vec学习结构化节点嵌入，并采用带自动聚类数估计的K-Means聚类获取楼层划分。该框架在多个公开数据集上进行评估，包括新发布的华为大学挑战2021数据集和重构版UJIIndoorLoc基准。实验结果表明，该方法仅利用接收信号强度数据即可有效捕捉多层建筑的内在垂直结构。通过消除对建筑元数据的依赖，所提方法为室内环境中的垂直定位提供了可扩展且实用的解决方案。
原文摘要 · Abstract (English)
Vertical localization, particularly floor separation, remains a major challenge in indoor positioning systems operating in GPS-denied multistory environments. This paper proposes a fully data-driven, graph-based framework for blind floor separation using only Wi-Fi fingerprint trajectories, without requiring prior building information or knowledge of the number of floors. In the proposed method, Wi-Fi fingerprints are represented as nodes in a trajectory graph, where edges capture both signal similarity and sequential movement context. Structural node embeddings are learned via Node2Vec, and floor-level partitions are obtained using K-Means clustering with automatic cluster number estimation. The framework is evaluated on multiple publicly available datasets, including a newly released Huawei University Challenge 2021 dataset and a restructured version of the UJIIndoorLoc benchmark. Experimental results demonstrate that the proposed approach effectively captures the intrinsic vertical structure of multistory buildings using only received signal strength data. By eliminating dependence on building-specific metadata, the proposed method provides a scalable and practical solution for vertical localization in indoor environments.
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