| Summary: | With the increasing demands of location-based services, indoor positioning systems based on the received signal strength (RSS) fingerprinting and pedestrian dead reckoning (PDR) have been attracting lots of research interests in both academia and industry. However, the RSS fingerprinting suffers from the burdensome site survey for radio map construction, while the PDR tracking suffers from the accumulative step localization error. In this paper, we propose the RMapTAFA scheme to construct a radio map from pedestrian trajectories to jointly address the two challenges. We first propose a novel sample-fingerprint structure containing two new coefficients for fingerprint composition: the credibility coefficient measures the confidence level of a sample, while the reliability coefficient defines the importance of each element in a fingerprint. Each step RSS sample of a pedestrian trajectory, if being determined eligible, is included into the proposed structure, together with its credibility computed from our proposed fingerprint amendment algorithm. Furthermore, we propose a trajectory adjustment algorithm via selective particle filtering by enjoying the RSS-fingerprinting result obtained from the constructed radio map. Field measurements and experiments validate the RMapTAFA scheme in terms of the improved localization performance of both pedestrian trajectory tracking and stationary point positioning.
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