Mobile Computing, Applications, and Services. Third International Conference, MobiCASE 2011, Los Angeles, CA, USA, October 24-27, 2011. Revised Selected Papers

Research Article

Probabilistic Infrastructureless Positioning in the Pocket

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  • @INPROCEEDINGS{10.1007/978-3-642-32320-1_20,
        author={Le Nguyen and Ying Zhang},
        title={Probabilistic Infrastructureless Positioning in the Pocket},
        proceedings={Mobile Computing, Applications, and Services. Third International Conference, MobiCASE 2011, Los Angeles, CA, USA, October 24-27, 2011. Revised Selected Papers},
        proceedings_a={MOBICASE},
        year={2012},
        month={10},
        keywords={Inertial positioning low-cost inertial sensors Dead Reckoning Bayes’ theorem Expectation Maximization},
        doi={10.1007/978-3-642-32320-1_20}
    }
    
  • Le Nguyen
    Ying Zhang
    Year: 2012
    Probabilistic Infrastructureless Positioning in the Pocket
    MOBICASE
    Springer
    DOI: 10.1007/978-3-642-32320-1_20
Le Nguyen1,*, Ying Zhang1,*
  • 1: Carnegie Mellon University
*Contact email: le.nguyen@sv.cmu.edu, joy.zhang@sv.cmu.edu

Abstract

With the increasing popularity of smart phones, knowing the accurate position of users has become critical to many context-aware applications. In this paper, we introduce a novel Probabilistic Infrastructureless Navigation (ProbIN) system for GPS-challenging environments. ProbIN uses inertial and magnetic sensors in mobile phones to derive users’ current location. Instead of relying on basic laws of physics (e.g. double integral of acceleration equals to displacement) ProbIN uses a statistical model for estimating the position of users. This statistical model is built based on the user’s data by applying machine learning techniques from the statistical machine translation field. Thus, ProbIN can capture the user’s specific walking patterns and is, therefore, more robust against noisy sensor readings. In the evaluation of our approach we focused on the most common daily scenarios. We conducted experiments with a user walking and carrying the phone in different settings such as in the hand or in the pocket. The results of the experiments show that even though the mobile phone was not mounted to the user’s body, ProbIN outperforms the state-of-the-art dead reckoning approaches.