A Wi-Fi indoor positioning modeling based on location fingerprint and cluster analysis

Abstract

Wi-Fi indoor positioning modeling based on location fingerprint and cluster analysis is studied. Specific locations are calculated by using RSSI nearest neighbor estimation method, and the positioning accuracies of different terminals are compared. The RSSI signal intensity is used to make clustering process for the fingerprint database. The noise signal in the fingerprint database is filtered. The traditional location fingerprint database, probability estimation fingerprint database and improved clustering algorithm fingerprint database are established. By comparing the positioning error of the testing data in three different fingerprint databases, the accuracy of indoor positioning is improved. Finally, the Wi-Fi data receiving module, the positioning server module and the positioning display module of positioning terminal are established, and the positioning APP is tested in the actual environment.