Exploiting environmental information using HsMMs for smartphone user tracking

Publication Name

IEEE Sensors Journal


The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference of staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This paper proposes an online HsMM forward recursion algorithm to incorporate these information for real-time smartphone user tracking. We apply the proposed HsMM forward recursion algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.

Open Access Status

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