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Radio tomographic imaging and tracking of stationary and moving people via kernel distance

  • Yang Zhao*
  • , Neal Patwari
  • , Jeff M. Phillips
  • , Suresh Venkatasubramanian
  • *Corresponding author for this work
  • General Electric
  • University of Utah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network radio frequency (RF) environment sensing (NRES) systems pinpoint and track people in buildings using changes in the signal strength measurements made by a wireless sensor network. It has been shown that such systems can locate people who do not participate in the system by wearing any radio device, even through walls, because of the changes that moving people cause to the static wireless sensor network. However, many such systems cannot locate stationary people. We present and evaluate a system which can locate stationary or moving people, without calibration, by using kernel distance to quantify the difference between two histograms of signal strength measurements. From five experiments, we show that our kernel distance-based radio tomographic localization system performs better than the state-of-the-art NRES systems in different non line-of-sight environments.

Original languageEnglish
Title of host publicationIPSN 2013 - Proceedings of the 12th International Conference on Information Processing in Sensor Networks, Part of CPSWeek 2013
Pages229-240
Number of pages12
DOIs
StatePublished - 2013
Externally publishedYes
Event12th International Conference on Information Processing in Sensor Networks, IPSN 2013 - Part of CPSWeek 2013 - Philadelphia, PA, United States
Duration: 8 Apr 201311 Apr 2013

Publication series

NameIPSN 2013 - Proceedings of the 12th International Conference on Information Processing in Sensor Networks, Part of CPSWeek 2013

Conference

Conference12th International Conference on Information Processing in Sensor Networks, IPSN 2013 - Part of CPSWeek 2013
Country/TerritoryUnited States
CityPhiladelphia, PA
Period8/04/1311/04/13

Keywords

  • Localization
  • Sensor networks
  • Tracking

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