A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

Abstract

Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization as low localization time and higher localization accuracy are the main objectives of any localization system.