5th International ICST Conference on Communications and Networking in China

Research Article

Self-localization in wireless sensor networks using particle filtering with progressive correction

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  • @INPROCEEDINGS{10.4108/chinacom.2010.63,
    author={Thomas Hanselmann and Yu Zhang and Mark Morelande and Mohd Ifran Md Nor and Jonathan Wei Jen Tan and Xing-She Zhou and Yee Wei Law},
    title={Self-localization in wireless sensor networks using particle filtering with progressive correction},
    proceedings={5th International ICST Conference on Communications and Networking in China},
    publisher={IEEE},
    proceedings_a={CHINACOM},
    year={2011},
    month={1},
    keywords={Antenna measurements Antennas Atmospheric measurements Bayesian methods IP networks Noise measurement Particle measurements},
    doi={10.4108/chinacom.2010.63}
}
  • Thomas Hanselmann
    Yu Zhang
    Mark Morelande
    Mohd Ifran Md Nor
    Jonathan Wei Jen Tan
    Xing-She Zhou
    Yee Wei Law
    Year: 2011
    Self-localization in wireless sensor networks using particle filtering with progressive correction
    CHINACOM
    ICST
    DOI: 10.4108/chinacom.2010.63
Thomas Hanselmann1,*, Yu Zhang2,*, Mark Morelande1, Mohd Ifran Md Nor1, Jonathan Wei Jen Tan1, Xing-She Zhou2,*, Yee Wei Law1
  • 1: Dept. EEE, The University of Melbourne, Parkville, VIC 3010, Australia
  • 2: School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
*Contact email: t.hanselmann@ee.unimelb.edu.au, zhangyu@nwpu.edu.cn, zhouxs@nwpu.edu.cn

Abstract

A centralized self-localization algorithm is used to estimate sensor locations. From the known positions of at least 3 anchor nodes the remaining sensor positions are estimated using an efficient particle filter (PF) with progressive correction. The measurement model is a simple two-parameter log-normal shadowing model, where the parameters are estimated concurrently. Experiments using Crossbow Imote2 motes show that an error of less than 16% is achievable in an indoor environment. The results demonstrate that by using PF with progressive correction, a small number of measurements and a simple signal propagation model are sufficient to give low localization errors.

Keywords
Antenna measurements Antennas Atmospheric measurements Bayesian methods IP networks Noise measurement Particle measurements
Published
2011-01-10
Publisher
IEEE
http://dx.doi.org/10.4108/chinacom.2010.63
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