Abstract
This letter addresses the problem of wireless indoor positioning and proposes the concept of personal mobility map (PMM). As a graph describing an individual's typical mobility pattern over a physical or abstract space, PMM can be used as the building block for crowd-sourcing based simultaneous localization and mapping (SLAM). PMM can also enable new kinds of location-based services (LBSs). To construct a PMM, we conduct intra-sequence clustering on the sequences of Wi-Fi received signal strength (RSS) measurements to cluster the measurements into different location points (LPs), followed by personal inter-sequence LP assembling, which aligns these LPs with the concept of the Smith-Waterman algorithm originally used for protein sequencing. Experiments with Wi-Fi RSS measurements collected in the university campus prove that the PMM performs well in indoor mapping and path estimation.
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