SCPL: indoor device-free multi-subject counting and localization using radio signal strength

  • Authors:

  • Chenren Xu;Bernhard Firner;Robert S. Moore;Yanyong Zhang;Wade Trappe;Richard Howard;Feixiong Zhang;Ning An

  • Affiliations:

  • Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Rutgers University, PISCATAWAY, NJ, USA;Hefei University of Technology, Hefei, China

  • Venue:
  • Proceedings of the 12th international conference on Information processing in sensor networks
  • Year:
  • 2013

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Abstract

Radio frequency based device-free passive (DfP) localization techniques have shown great potentials in localizing individual human subjects, without requiring them to carry any radio devices. In this study, we extend the DfP technique to count and localize multiple subjects in indoor environments. To address the impact of multipath on indoor radio signals, we adopt a fingerprinting based approach to infer subject locations from observed signal strengths through profiling the environment. When multiple subjects are present, our objective is to use the profiling data collected by a single subject to count and localize multiple subjects without any extra effort. In order to address the non-linearity of the impact of multiple subjects, we propose a successive cancellation based algorithm to iteratively determine the number of subjects. We model indoor human trajectories as a state transition process, exploit indoor human mobility constraints and integrate all information into a conditional random field (CRF) to simultaneously localize multiple subjects. As a result, we call the proposed algorithm SCPL -- sequential counting, parallel localizing. We test SCPL with two different indoor settings, one with size 150 m2 and the other 400 m2. In each setting, we have four different subjects, walking around in the deployed areas, sometimes with overlapping trajectories. Through extensive experimental results, we show that SCPL can count the present subjects with 86% counting percentage when their trajectories are not completely overlapping. Our localization algorithms are also highly accurate, with an average localization error distance of 1.3 m.