A Boundary Aware WiFi Localization Scheme Based on UMAP and KNN

In this letter, a novel localization scheme is reported to improve WiFi fingerprint-based localization via dimensionality reduction and an adaptive fingerprint matching algorithm. Specifically, an experimental analysis is firstly carried out to investigate the quality of radio maps. Then, the advanced uniform manifold approximation and projection (UMAP) algorithm is applied to refine the mapping relations from a fingerprint space to a location space in a supervised dimensionality reduction approach, which significantly improves the quality of radio maps. In addition, to further enhance the localization accuracy, boundary aware KNN (B-KNN) is proposed to adaptively deal with fingerprint matching according to inner area or boundary area. Extensive experiments are conducted in a real scenario with the area of nearly $1000~m^{2}$ , and a thorough comparison with several existing popular methods illustrates that the proposed scheme can significantly improve the localization accuracy on average by 33.3%.