Marvel: Mann-Whitney Rank-Sum Testing via Segments Labeling for Indoor Pedestrian Localization

The rapid development of ubiquitous and high-speed wireless communication technology has driven the increasingly serious demand for the Location-based Services (LBSs). In this circumstance, we propose a new crowd-sourced calibration-free and inertial sensor- independent indoor pedestrian localization approach, namely Mann-Whitney rank-sum testing via segments labeling (Marvel). In concrete terms, first of all, the motion paths are modeled by using the A* algorithm with the floor plan provided by the merchant, and then each motion path is segmented according to the preset expected localization accuracy. Second, by setting the signal similarity threshold, the Received Signal Strength (RSS) sequences which are collected by the human subjects following their daily routines in target environment are also segmented. Third, the proposed Marvel is adopted to cluster the motion path segments as well as RSS sequence segments respectively to construct the physical and signal logic graphs. Finally, by using the concept of backbone nodes diffusion mapping to establish the mapping relations between the physical and signal spaces, the pedestrian localization and the related motion analysis are conducted by the server. Furthermore, the extensive experimental results show that the proposed approach is capable of achieving higher localization accuracy compared with the current state-of-the-art approaches.