Bayesian indoor positioning systems

David Madigan, Eiman Elnahrawy, Richard P. Martin, Wen Hua Ju, P. Krishnan, A. S. Krishnakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

337 Scopus citations

Abstract

In this paper, we introduce a new approach to location estimation where, instead of locating a single client, we simultaneously locate a set of wireless clients. We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model eliminates the requirement for training data as compared with existing approaches, thereby introducing the notion of a fully adaptive zero profiling approach to location estimation.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE INFOCOM 2005. The Conference on Computer Communications - 24th Annual Joint Conference of the IEEE Computer and Communications Societies
EditorsK. Makki, E. Knightly
Pages1217-1227
Number of pages11
DOIs
StatePublished - 2005
EventIEEE INFOCOM 2005 - Miami, FL, United States
Duration: Mar 13 2005Mar 17 2005

Publication series

NameProceedings - IEEE INFOCOM
Volume2
ISSN (Print)0743-166X

Other

OtherIEEE INFOCOM 2005
Country/TerritoryUnited States
CityMiami, FL
Period3/13/053/17/05

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Keywords

  • Bayesian graphical models
  • Experimentation with real networks/Testbed
  • Localization
  • RSS/fingerprinting
  • Statistics
  • WLAN

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