WiFi Positioning Based on User Orientation Estimation and Smartphone Carrying Position Recognition

Abstract

Accuracy performance of WiFi fingerprinting positioning systems deteriorates severely when signal attenuations caused by human body are not considered. Previous studies have proposed WiFi fingerprinting positioning based on user orientation using compasses built in smartphones. However, compasses always cannot provide required accuracy of user orientation estimation due to the severe indoor magnetic perturbations. More importantly, we discover that not only user orientations but also smartphone carrying positions may affect signal attenuations caused by human body greatly. Therefore, we propose a novel WiFi fingerprinting positioning approach considering both user orientations and smartphone carrying positions. For user orientation estimation, we deploy Rotation Matrix and Principal Component Analysis (RMPCA) approach. For carrying position recognition, we propose a robust Random Forest classifier based on the developed orientation invariant features. Experimental results show that the proposed WiFi positioning approach may improve positioning accuracy significantly.