Human localization based on inertial sensors and fingerprints in the Industrial Internet of Things

Localization services, especially for human localization, are an indispensable component of most technologies and applications related to the Industrial Internet of Things (IIoT). However, because of the complexity of an industrial environment and the mobility of the subjects, attempts to develop an accurate localization solution face certain difficulties. In this paper, we propose a novel approach that leverages the inertial sensors embedded in smartphones and uses WiFi fingerprints based on the Angle of Arrival (AoA) to assist in localization; this approach is referred to as ISWF for short. By using data from inertial sensors in smartphones and with the supplementary incorporation of fingerprint localization, our approach can overcome the difficulties posed by complex human movements and magnetic interference in an industrial environment. To accurately localize the user's position, a step length map is designed; its training process includes step boundary detection, step length estimation and orientation angle estimation. Experiments in a realistic environment show that the presented method demonstrates superior localization performance compared with the existing methods.