Bayesian optimized indoor positioning algorithm based on dual clustering.

ABSTRACT: Wi-Fi indoor positioning provides a simple, convenient, ubiquitous and cost-effective solution by matching a pre-established Wi-Fi Received Signal Strength Indication (RSSI) fingerprint database with the RSSI values received from mobile terminals. However, due to the influence of the complex indoor environment on the signal, its accuracy can only reach the meter scale, and the huge fingerprint database leads to inefficient positioning. To solve this problem, the Canopy algorithm is used for coarse clustering, and then the K-means algorithm is used for fine clustering to determine the number of clusters and the initial clustering center to form multiple clustering sub-bases, which improves the positioning efficiency by about 95.05%. In the real-time matching stage, the sub-banks with the hi

SUBMITTER: Chen M 

PROVIDER: S-EPMC11920591 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature