Indoor localization using smartphone inertial sensors

Celebrated fingerprinting techniques localize users by statistically learning the signal to location relations. However, collecting a lot of labelled data to train an accurate localization model is expensive and labour-intensive. In this paper, an economic and easy-to-deploy indoor localization model suitable for ubiquitous smartphone platforms is established. The method processes embedded inertial sensors readings through a inertial localization system. A particle filter is developed to integrate the building map constraints and inertial localization results to estimate user's location. To increase the algorithm convergence rate, the user's initial/on-line room-level localization is achieved using WiFi signals. To achieve room-level accuracy, only very few training WiFi data, i.e. one per room or per segment of a corridor, are required. A novel crowdsourcing technique to build and update training database is presented. On these basis, an indoor localization system is proposed and evaluated. The results show that comparable location accuracy to previous approaches without even dense wireless site survey requirements is achievable.