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.

翻译：垂直定位，特别是楼层分离，在无GPS的多层室内定位系统中仍是一个主要挑战。本文提出了一种完全数据驱动的、基于图的框架，用于仅利用Wi-Fi指纹轨迹进行盲楼层分离，无需先验建筑信息或楼层数量知识。在所提出的方法中，Wi-Fi指纹被表示为轨迹图中的节点，其中边同时捕捉信号相似性和连续移动上下文。通过Node2Vec学习结构节点嵌入，并利用带自动聚类数估计的K-Means聚类获得楼层级分区。该框架在多个公开数据集上进行了评估，包括新发布的华为大学挑战赛2021数据集和重构的UJIIndoorLoc基准。实验结果表明，所提出的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。通过消除对建筑特定元数据的依赖，该方法为室内环境中的垂直定位提供了一个可扩展且实用的解决方案。