Journal
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
Volume 45, Issue 5, Pages 562-574Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2014.2368092
Keywords
Activity sequence; hidden Markov model (HMM); indoor localization; smartphone
Funding
- Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program [JCYJ20121019111128765]
- Shenzhen Scientific Research and Development Funding Program [ZDSY20121019111146499, JSGG20121026111056204, JCYJ20120817163755063, JCYJ20140418095735587]
- Major State Basic Research Development Program [2010CB732100]
- National Natural Science Foundation of China [41201483, 41301511, 41401444]
- China Postdoctoral Science Foundation [2013M542199, 2014M560671]
- Navinfo Innovation Funding Program
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This paper presents an activity sequence-based indoor pedestrian localization approach using smartphones. The activity sequence consists of several continuous activities during the walking process, such as turning at a corner, taking the elevator, taking the escalator, and walking stairs. These activities take place when a user walks at some special points in the building, like corners, elevators, escalators, and stairs. The special points form an indoor road network. In our approach, we first detect the user's activities using the built-in sensors in a smartphone. The detected activities constitute the activity sequence. Meanwhile, the user's trajectory is reckoned by Pedestrian Dead Reckoning (PDR). Based on the detected activity sequence and reckoned trajectory, we realize pedestrian localization by matching them to the indoor road network using a Hidden Markov Model. After encountering several special points, the location of the userwould converge on the true one. We evaluate our proposed approach using smartphones in two buildings: an office building and a shopping mall. The results show that the proposed approach can realize autonomous pedestrian localization even without knowing the initial point in the environments. The mean offline localization error is about 1.3 m. The results also demonstrate that the proposed approach is robust to activity detection error and PDR estimation error.
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