TY  - INPR
ID  - SisLab2796
UR  - http://atc-conf.org/
A1  - Hoang, Manh Kha
A1  - Duong, Thi Hang
A1  - Vu, Trung Kien
A1  - Trinh, Anh Vu
Y1  - 2017/10/18/
N2  - 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.
TI  - Enhancing WiFi based Indoor Positioning by Modeling Measurement Data with GMM
SP  - 325
M2  - Quy Nhon, Viet Nam
AV  - none
EP  - 328
T2  - International Conference on Advanced Technologies for Communications
ER  -