基于图的楼层分离使用节点嵌入和WiFi轨迹聚类
Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories
AI总结：
本文提出一种基于图和节点嵌入的WiFi轨迹聚类方法，用于无需建筑信息的盲楼层分离，通过信号相似性和移动上下文建模，实现多层建筑的垂直定位。
摘要：
垂直定位，特别是楼层分离，仍然是在GPS受限的多层室内定位系统中的一大挑战。本文提出了一种完全数据驱动的、基于图的框架，用于盲目的楼层分离，仅使用Wi-Fi指纹轨迹，不需要先前的建筑信息或楼层数量的知识。在所提出的方法中，Wi-Fi指纹被表示为轨迹图中的节点，其中边捕捉信号相似性和顺序移动上下文。通过Node2Vec学习结构节点嵌入，并使用带有自动聚类数估计的K-Means聚类获得楼层级分区。该框架在多个公开可用的数据集上进行评估，包括新发布的华为大学挑战2021数据集和重新整理的UJIIndoorLoc基准数据集。实验结果表明，所提出的方法仅使用接收到的信号强度数据，就能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，所提出的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。
英文摘要：
Indoor positioning systems (IPSs) are increasingly vital for location-based services in complex multi-storey environments. This study proposes a novel graph-based approach for floor separation using Wi-Fi fingerprint trajectories, addressing the challenge of vertical localization in indoor settings. We construct a graph where nodes represent Wi-Fi fingerprints, and edges are weighted by signal similarity and contextual transitions. Node2Vec is employed to generate low-dimensional embeddings, which are subsequently clustered using K-means to identify distinct floors. Evaluated on the Huawei University Challenge 2021 dataset, our method outperforms traditional community detection algorithms, achieving an accuracy of 68.97%, an F1- score of 61.99%, and an Adjusted Rand Index of 57.19%. By publicly releasing the preprocessed dataset and implementation code, this work contributes to advancing research in indoor positioning. The proposed approach demonstrates robustness to signal noise and architectural complexities, offering a scalable solution for floor-level localization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
