From Global to Local: Cluster-Aware Learning for Wi-Fi Fingerprinting Indoor Localisation
This work addresses indoor positioning challenges for practical applications like navigation in large buildings, but it is incremental as it builds on existing clustering and machine learning techniques.
The paper tackles the problem of degraded accuracy in Wi-Fi fingerprinting indoor localization due to dataset heterogeneity and environmental ambiguity by introducing a clustering-based method that groups fingerprints before localization, resulting in a consistent reduction in localization errors across three public datasets, though with a trade-off in floor detection accuracy.
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.