Metric Learning Aware Clustering For Wi‐Fi Fingerprint‐Based Indoor Localisation

ABSTRACT

Reliable indoor positioning is crucial for location‐based services, especially in complex buildings with multiple floors. Although Wi‐Fi fingerprinting is cost‐effective, it still has major limitations, including signal instability and inter‐floor errors. Clustering‐based methods are the mainstream solution to solve this problem. However, traditional distance metrics fail to capture the complexity of indoor environments and have limitations in identifying non‐linear relationships. To address these issues, we present a feature‐enhanced clustering framework built on the Wi‐Fi signal embedding neural network (WiSE‐Net) and a hierarchical‐polarised weighted K‐nearest neighbour (HP‐WKNN) algorithm. Extensive experiments on three public benchmark datasets (UJIIndoorLoc, Tampere and UTSIndoorLoc) indicate that our method achieves the lowest mean error of 6.18 m and maintains the highest floor detection rate (FDR) of 94.81%. This work provides a reliable solution for large‐scale multi‐building, multi‐floor indoor localisation, improving both positioning stability and accuracy across diverse environments.