| Summary: | A Reference Fingerprinting Map (RFM) is the basis for fingerprinting-based Wifi positioning. The quality of RFM is one of the major factors for positioning accuracy. The RFM constantly changes in many dynamic indoor environments and needs to be updated accordingly. The problem of keeping the RFM up-to-date is referred to as the RFM recalibration problem. The key to the RFM recalibration problem is to annotate the collected fingerprints with coordinate locations. Existing methods can be divided into two categories: (1) adopting external measurements (e.g. user-contributed positions) or external hardwares; (2) only adopting the measurements available from a common commercial off-the-shelf (COTS) smartphone. In this paper, a crowd-sourced RFM recalibration method is proposed adopting particles filters. The proposed method belongs to the second category, which has the advantage of independence from human intervention or additional hardwares. In the proposed method, the fingerprints in the RFM denote on-off values showing the availability of access points (APs) rather than the actual Received Signal Strength (RSS) values. Particle filters (implemented per-user data) are adopted for fusing the information of Pedestrian Dead Reckoning (PDR) and Wifi-based positioning results. The quality of the estimated trajectory can be indicated through the divergence of the particles. The trajectories with large particle divergence are discarded, and otherwise, a particle filter based smoothing technique is adopted to backtrack or re-estimate the trajectories to make them more accurate. Then the re-estimated trajectories can be adopted to recalibrate the existing RFM. From the designed experiments, we show that (1) the proposed method is effective for RFM recalibration; (2) although consumes more running time, the proposed method has better performance than the classical Radio Map Automatic Annotation (RMAA) and the Participatory Indoor Localization System (Piloc) methods.
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