Accurate Moving Distance Estimation via Multi-modal Fusion from IMU Sensors and WiFi Signal

Abstract

Moving distance measurement is an indispensable component for the indoor localization and user trace tracking, which is of great importance to a wide range of applications in the era of mobile computing. The maturity of inertial sensors in smartphones and the ubiquity of WiFi technology ensure the accuracy for indoor distance measurement. Despite its importance, moving distance estimation in the indoor environment for mobile devices is still lacking a cost-effective and precise solution. The state-of-the-art work mostly use build-in sensors, e.g. accelerometer, gyroscope, rotation vector sensor and etc. in the mobile devices for the movement distance measurement. Wireless signal is considered to estimate a humans moving distance as well in prior work. However, both methods suffer from complex deployment and inaccurate estimation results. In this paper, we propose a multi-modal approach to measure moving distance for the user. We mainly innovate in proposing a fusion estimation method leveraging sensors and wireless signals to accurately estimate the human’s moving distance indoor. We implement a prototype with smartphones and commercial WiFi devices. Then we evaluate it in distinct indoor environments. Experimental results show that the proposed method can estimate target’s moving distance with an average accuracy of 90.7%, which sheds light on sub-meter level distance measurements in indoor environments.