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

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7 Author(s)
Thomas Hanselmann ; Dept. EEE, The University of Melbourne, Parkville, VIC 3010, Australia ; Yu Zhang ; Mark Morelande ; Mohd Ifran Md Nor
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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.

Published in:

Communications and Networking in China (CHINACOM), 2010 5th International ICST Conference on

Date of Conference:

25-27 Aug. 2010