Sequential Monte Carlo filtering for location estimation in indoor wireless environments

Jihoon Ryoo, Hyunjun Choi, Hwangnam Kim

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

1 Citation (Scopus)

Abstract

In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm [1] in terms of the distance estimation error.

Original languageEnglish
Title of host publication2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010
DOIs
Publication statusPublished - 2010
Event2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010 - Las Vegas, NV, United States
Duration: 2010 Jan 92010 Jan 12

Publication series

Name2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010

Other

Other2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010
CountryUnited States
CityLas Vegas, NV
Period10/1/910/1/12

Keywords

  • Sequential Monte Carlo method
  • WiFi radio signal strength based localization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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    Ryoo, J., Choi, H., & Kim, H. (2010). Sequential Monte Carlo filtering for location estimation in indoor wireless environments. In 2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010 [5421650] (2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010). https://doi.org/10.1109/CCNC.2010.5421650