Wi-Fi fingerprinting remains one of the most practical solutions for indoor positioning, however, its performance is often limited by the size and heterogeneity of fingerprint datasets, strong Received Signal Strength Indicator variability, and the ambiguity introduced in large and multi-floor environments. These factors significantly degrade localisation accuracy, particularly when global models are applied without considering structural constraints. This paper introduces a clustering-based method that structures the fingerprint dataset prior to localisation. Fingerprints are grouped using either spatial or radio features, and clustering can be applied at the building or floor level. In the localisation phase, a clustering estimation procedure based on the strongest access points assigns unseen fingerprints to the most relevant cluster. Localisation is then performed only within the selected clusters, allowing learning models to operate on reduced and more coherent subsets of data. The effectiveness of the method is evaluated on three public datasets and several machine learning models. Results show a consistent reduction in localisation errors, particularly under building-level strategies, but at the cost of reducing the floor detection accuracy. These results demonstrate that explicitly structuring datasets through clustering is an effective and flexible approach for scalable indoor positioning.

翻译：Wi-Fi指纹识别仍然是室内定位最实用的解决方案之一，但其性能常受限于指纹数据集规模与异质性、接收信号强度指示器的高变异性，以及大型多楼层环境带来的模糊性。这些因素显著降低了定位精度，尤其在应用全局模型而未考虑结构约束时更为突出。本文提出一种基于聚类的方法，在定位前对指纹数据集进行结构化处理。通过空间或射频特征对指纹进行分组，聚类可在建筑物或楼层级别实施。在定位阶段，基于最强接入点的聚类估计流程将未见指纹分配至最相关的簇中，随后仅在选定簇内执行定位，使得学习模型能够在缩减后更具一致性的数据子集上运行。该方法在三个公开数据集和多种机器学习模型上进行了效能评估。结果表明，该方法能持续降低定位误差（尤其在建筑物级策略下），但会以降低楼层检测精度为代价。这些结果证明，通过聚类显式构建数据集是一种有效且灵活的、可扩展的室内定位方法。