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An accurate indoor map matching algorithm based on activity detection and crowdsourced Wi-Fi

  • WenPing Yu
  • JianZhong ZhangEmail author
  • JingDong Xu
  • YuWei Xu
Article
  • 6 Downloads

Abstract

Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness. This paper proposes an accurate map matching algorithm based on activity detection and crowdsourced Wi-Fi (AiFiMatch). Firstly, by taking indoor road segments between activity-related locations as nodes, and the activity type from one road segment to another as directed edge, the indoor floor plan is abstracted as a directed graph. Secondly, the smartphone’s motion sensors are utilized to detect different activities based on a decision tree and then the pedestrian’s walking trajectory is divided into subtrajectory sequence according to location-related activities. Finally, the sub-trajectory sequence is matched to the directed graph of indoor floor plan to position the pedestrian by using a Hidden Markov Model (HMM). Simultaneously, Wi-Fi fingerprints are bound to road segments based on timestamp. Through crowdsourcing, a radio map of indoor road segments is constructed. The radio map in turn inversely promotes the HMM based map matching algorithm. AiFiMatch is evaluated by the experiments using smartphones in a teaching building. Experimental results show that the pedestrian can be accurately tracked even without knowing the starting position and AiFiMatch is robust to a certain degree of step length and heading direction errors.

Keywords

map matching hidden markov model activity detection crowdsourced Wi-Fi 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • WenPing Yu
    • 1
  • JianZhong Zhang
    • 1
    Email author
  • JingDong Xu
    • 1
  • YuWei Xu
    • 1
  1. 1.College of Computer ScienceNankai UniversityTianjinChina

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