Research Article
Self-localization in wireless sensor networks using particle filtering with progressive correction
@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
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.
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