%A Manh Kha Hoang
%A Thi Hang Duong
%A Trung Kien Vu
%A Anh Vu Trinh
%T Enhancing WiFi based Indoor Positioning by Modeling Measurement Data with GMM
%X Abstract—Recently, indoor positioning has been investigated by many research groups. Several proposals for indoor positioning have been developed over the last decades, amongst them, WiFi fingerprinting based is considered as the most promissing approach. However, many challenges are still remained for improving the accuracy of indoor mobile object localization. This paper presents a novel approach for enhancement of WiFi fingerprinting based indoor positioning by using Gaussian Mixture Model (GMM) to model the measured Received Signal Strength Index (RSSI) distributions. Since the measured data
varies due to obstacles in indoor environments as well as user directions, the proposed approach is able to model the data distribution more precise than the previous popular approach which utilizes single Gaussian. Simulation results demonstrate the effectiveness of the proposed approach compared to the others.
%C Quy Nhon, Viet Nam
%D 2017
%P 325-328
%L SisLab2796